├── .gitignore ├── Apresentacoes ├── 2019_Capyra │ └── grupo_de_estudos_estimula_troca_de_saber_Pyladies_SP.pdf └── 2021_Python_Brasil │ ├── PyBR_2021_Machine_Learning_do_início_ao_fim.pdf │ └── Roteiro_7passos.pdf ├── Evento_Ciencia_Dados_Python ├── Acessando APIs em Python.pdf ├── Algoritmo_não_supervisionado_K_Means_.ipynb ├── Algoritmos Não Supervisionados - PyLadies.ppt ├── Começando do Zero na Análise de Dados com.pdf ├── Ingestão de dados com Python.pdf ├── Por onde começar a estudar Ciência de Dados_.pdf └── pyladies - redes neurais.pdf ├── Links_uteis ├── Bases_de_dados │ └── README.md └── Textos_sobre_Ciencia_de_Dados │ └── README.md ├── Oficinas ├── oficina_introdução_estatistica_pandas │ ├── Workshop Introdução a Estatística e Pandas Respostas.pdf │ ├── tips.csv │ ├── tips.ipynb │ └── tips_notebook.ipynb └── oficina_introdução_nlp │ ├── 1-Relembrando-Python-Com_Respostas.ipynb │ ├── 2-Intro_NLP-Com_Respostas.ipynb │ └── PYLADIES -NLP.pdf ├── README.md ├── Trilha_de_Estudos ├── 01 material.pdf ├── 02 material.pdf ├── 03 material.pdf ├── 04 material.pdf ├── 05 material.pdf ├── 06 material.pdf ├── 06 notebook.ipynb ├── 07 material.pdf ├── 07 notebook.ipynb ├── 08 material.pdf ├── 08 notebook.ipynb ├── 09 material.pdf ├── 09 notebook.ipynb ├── Cronograma Estudos - GEDS.xlsx ├── Desafio Completo_ Valor de Venda Imóveis.pdf ├── README.md ├── folhaprodam2018_anonima.csv ├── proposta_estudo.pdf └── respostas_material_02.ipynb ├── _config.yml ├── licenca.md └── logo.jpg /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /Apresentacoes/2019_Capyra/grupo_de_estudos_estimula_troca_de_saber_Pyladies_SP.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Apresentacoes/2019_Capyra/grupo_de_estudos_estimula_troca_de_saber_Pyladies_SP.pdf -------------------------------------------------------------------------------- /Apresentacoes/2021_Python_Brasil/PyBR_2021_Machine_Learning_do_início_ao_fim.pdf: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Evento_Ciencia_Dados_Python/pyladies - redes neurais.pdf -------------------------------------------------------------------------------- /Links_uteis/Bases_de_dados/README.md: -------------------------------------------------------------------------------- 1 | # Bases de Dados 2 | 3 | Bases de dados disponíveis para análises de Ciências de Dados. 4 | 5 | Você pode contribuir com esse repositório com novas bases com novos pull requests! 6 | 7 | ### Bases de Dados 8 | 9 | - [Dados da Secretaria de Segurança de São Paulo](https://www.ssp.sp.gov.br/transparenciassp/) 10 | 11 | - [Preços de combustíveis no Brasil](https://www.kaggle.com/matheusfreitag/gas-prices-in-brazil) 12 | 13 | - [Preços de peças em três brechós de São Paulo](https://www.kaggle.com/mateuspgomes/brazil-thrift-stores-data) 14 | 15 | - [Preços de alugueis em São Paulo](https://www.kaggle.com/argonalyst/sao-paulo-real-estate-sale-rent-april-2019) 16 | 17 | - [Preços de alugueis no Brasil](https://www.kaggle.com/rubenssjr/brasilian-houses-to-rent) 18 | 19 | - [Vôos no Brasil](https://www.kaggle.com/ramirobentes/flights-in-brazil) 20 | 21 | - [Gastos de cotas de parlamentares](https://brasil.io/dataset/gastos-deputados/cota_parlamentar/) 22 | -------------------------------------------------------------------------------- /Links_uteis/Textos_sobre_Ciencia_de_Dados/README.md: -------------------------------------------------------------------------------- 1 | # Textos sobre Ciência de Dados 2 | 3 | Aqui reunimos links com textos com as mais variadas informações sobre Ciência de Dados. 4 | 5 | Você pode contribuir com esse repositório com novos textos com novos pull requests! 6 | 7 | ### Links de textos 8 | 9 | - [Como avaliar seu modelo de classificação](https://medium.com/data-hackers/como-avaliar-seu-modelo-de-classifica%C3%A7%C3%A3o-34e6f6011108) de Marcelo Randolfo 10 | 11 | 12 | -------------------------------------------------------------------------------- /Oficinas/oficina_introdução_estatistica_pandas/Workshop Introdução a Estatística e Pandas Respostas.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Oficinas/oficina_introdução_estatistica_pandas/Workshop Introdução a Estatística e Pandas Respostas.pdf -------------------------------------------------------------------------------- /Oficinas/oficina_introdução_estatistica_pandas/tips.csv: -------------------------------------------------------------------------------- 1 | total_conta,gorjeta,genero,fumante,dia,horario,pessoas_mesa,tempo_permanencia 2 | 16.99,1.01,Feminino,nao,dom,jantar,2,41 3 | 10.34,1.66,Masculino,nao,dom,jantar,3,40 4 | 21.01,3.5,Masculino,nao,dom,jantar,3,49 5 | 23.68,3.31,Masculino,nao,dom,jantar,2,43 6 | 24.59,3.61,Feminino,nao,dom,jantar,4,34 7 | 25.29,4.71,Masculino,nao,dom,jantar,4,39 8 | 8.77,2.0,Masculino,nao,dom,jantar,2,41 9 | 26.88,3.12,Masculino,nao,dom,jantar,4,31 10 | 15.04,1.96,Masculino,nao,dom,jantar,2,35 11 | 14.78,3.23,Masculino,nao,dom,jantar,2,41 12 | 10.27,1.71,Masculino,nao,dom,jantar,2,40 13 | 35.26,5.0,Feminino,nao,dom,jantar,4,38 14 | 15.42,1.57,Masculino,nao,dom,jantar,2,34 15 | 18.43,3.0,Masculino,nao,dom,jantar,4,36 16 | 14.83,3.02,Feminino,nao,dom,jantar,2,42 17 | 21.58,3.92,Masculino,nao,dom,jantar,2,39 18 | 10.33,1.67,Feminino,nao,dom,jantar,3,35 19 | 16.29,3.71,Masculino,nao,dom,jantar,3,40 20 | 16.97,3.5,Feminino,nao,dom,jantar,3,48 21 | 20.65,3.35,Masculino,nao,sab,jantar,3,40 22 | 17.92,4.08,Masculino,nao,sab,jantar,2,43 23 | 20.29,2.75,Feminino,nao,sab,jantar,2,44 24 | 15.77,2.23,Feminino,nao,sab,jantar,2,50 25 | 39.42,7.58,Masculino,nao,sab,jantar,4,42 26 | 19.82,3.18,Masculino,nao,sab,jantar,2,46 27 | 17.81,2.34,Masculino,nao,sab,jantar,4,37 28 | 13.37,2.0,Masculino,nao,sab,jantar,2,46 29 | 12.69,2.0,Masculino,nao,sab,jantar,2,41 30 | 21.7,4.3,Masculino,nao,sab,jantar,2,35 31 | 19.65,3.0,Feminino,nao,sab,jantar,2,37 32 | 9.55,1.45,Masculino,nao,sab,jantar,2,46 33 | 18.35,2.5,Masculino,nao,sab,jantar,4,39 34 | 15.06,3.0,Feminino,nao,sab,jantar,2,42 35 | 20.69,2.45,Feminino,nao,sab,jantar,4,42 36 | 17.78,3.27,Masculino,nao,sab,jantar,2,40 37 | 24.06,3.6,Masculino,nao,sab,jantar,3,46 38 | 16.31,2.0,Masculino,nao,sab,jantar,3,40 39 | 16.93,3.07,Feminino,nao,sab,jantar,3,41 40 | 18.69,2.31,Masculino,nao,sab,jantar,3,40 41 | 31.27,5.0,Masculino,nao,sab,jantar,3,37 42 | 16.04,2.24,Masculino,nao,sab,jantar,3,45 43 | 17.46,2.54,Masculino,nao,dom,jantar,2,32 44 | 13.94,3.06,Masculino,nao,dom,jantar,2,46 45 | 9.68,1.32,Masculino,nao,dom,jantar,2,43 46 | 30.4,5.6,Masculino,nao,dom,jantar,4,39 47 | 18.29,3.0,Masculino,nao,dom,jantar,2,35 48 | 22.23,5.0,Masculino,nao,dom,jantar,2,33 49 | 32.4,6.0,Masculino,nao,dom,jantar,4,41 50 | 28.55,2.05,Masculino,nao,dom,jantar,3,45 51 | 18.04,3.0,Masculino,nao,dom,jantar,2,42 52 | 12.54,2.5,Masculino,nao,dom,jantar,2,34 53 | 10.29,2.6,Feminino,nao,dom,jantar,2,42 54 | 34.81,5.2,Feminino,nao,dom,jantar,4,40 55 | 9.94,1.56,Masculino,nao,dom,jantar,2,46 56 | 25.56,4.34,Masculino,nao,dom,jantar,4,48 57 | 19.49,3.51,Masculino,nao,dom,jantar,2,37 58 | 38.01,3.0,Masculino,sim,sab,jantar,4,42 59 | 26.41,1.5,Feminino,nao,sab,jantar,2,34 60 | 11.24,1.76,Masculino,sim,sab,jantar,2,44 61 | 48.27,6.73,Masculino,nao,sab,jantar,4,47 62 | 20.29,3.21,Masculino,sim,sab,jantar,2,38 63 | 13.81,2.0,Masculino,sim,sab,jantar,2,42 64 | 11.02,1.98,Masculino,sim,sab,jantar,2,45 65 | 18.29,3.76,Masculino,sim,sab,jantar,4,43 66 | 17.59,2.64,Masculino,nao,sab,jantar,3,37 67 | 20.08,3.15,Masculino,nao,sab,jantar,3,39 68 | 16.45,2.47,Feminino,nao,sab,jantar,2,38 69 | 3.07,1.0,Feminino,sim,sab,jantar,1,48 70 | 20.23,2.01,Masculino,nao,sab,jantar,2,36 71 | 15.01,2.09,Masculino,sim,sab,jantar,2,44 72 | 12.02,1.97,Masculino,nao,sab,jantar,2,46 73 | 17.07,3.0,Feminino,nao,sab,jantar,3,45 74 | 26.86,3.14,Feminino,sim,sab,jantar,2,43 75 | 25.28,5.0,Feminino,sim,sab,jantar,2,35 76 | 14.73,2.2,Feminino,nao,sab,jantar,2,43 77 | 10.51,1.25,Masculino,nao,sab,jantar,2,32 78 | 17.92,3.08,Masculino,sim,sab,jantar,2,35 79 | 27.2,4.0,Masculino,nao,qui,almoco,4,47 80 | 22.76,3.0,Masculino,nao,qui,almoco,2,36 81 | 17.29,2.71,Masculino,nao,qui,almoco,2,38 82 | 19.44,3.0,Masculino,sim,qui,almoco,2,36 83 | 16.66,3.4,Masculino,nao,qui,almoco,2,37 84 | 10.07,1.83,Feminino,nao,qui,almoco,1,41 85 | 32.68,5.0,Masculino,sim,qui,almoco,2,38 86 | 15.98,2.03,Masculino,nao,qui,almoco,2,37 87 | 34.83,5.17,Feminino,nao,qui,almoco,4,41 88 | 13.03,2.0,Masculino,nao,qui,almoco,2,38 89 | 18.28,4.0,Masculino,nao,qui,almoco,2,32 90 | 24.71,5.85,Masculino,nao,qui,almoco,2,37 91 | 21.16,3.0,Masculino,nao,qui,almoco,2,37 92 | 28.97,3.0,Masculino,sim,sex,jantar,2,39 93 | 22.49,3.5,Masculino,nao,sex,jantar,2,35 94 | 5.75,1.0,Feminino,sim,sex,jantar,2,46 95 | 16.32,4.3,Feminino,sim,sex,jantar,2,36 96 | 22.75,3.25,Feminino,nao,sex,jantar,2,38 97 | 40.17,4.73,Masculino,sim,sex,jantar,4,41 98 | 27.28,4.0,Masculino,sim,sex,jantar,2,38 99 | 12.03,1.5,Masculino,sim,sex,jantar,2,30 100 | 21.01,3.0,Masculino,sim,sex,jantar,2,43 101 | 12.46,1.5,Masculino,nao,sex,jantar,2,38 102 | 11.35,2.5,Feminino,sim,sex,jantar,2,46 103 | 15.38,3.0,Feminino,sim,sex,jantar,2,47 104 | 44.3,2.5,Feminino,sim,sab,jantar,3,45 105 | 22.42,3.48,Feminino,sim,sab,jantar,2,45 106 | 20.92,4.08,Feminino,nao,sab,jantar,2,32 107 | 15.36,1.64,Masculino,sim,sab,jantar,2,42 108 | 20.49,4.06,Masculino,sim,sab,jantar,2,46 109 | 25.21,4.29,Masculino,sim,sab,jantar,2,34 110 | 18.24,3.76,Masculino,nao,sab,jantar,2,29 111 | 14.31,4.0,Feminino,sim,sab,jantar,2,41 112 | 14.0,3.0,Masculino,nao,sab,jantar,2,44 113 | 7.25,1.0,Feminino,nao,sab,jantar,1,37 114 | 38.07,4.0,Masculino,nao,dom,jantar,3,42 115 | 23.95,2.55,Masculino,nao,dom,jantar,2,41 116 | 25.71,4.0,Feminino,nao,dom,jantar,3,37 117 | 17.31,3.5,Feminino,nao,dom,jantar,2,35 118 | 29.93,5.07,Masculino,nao,dom,jantar,4,45 119 | 10.65,1.5,Feminino,nao,qui,almoco,2,36 120 | 12.43,1.8,Feminino,nao,qui,almoco,2,48 121 | 24.08,2.92,Feminino,nao,qui,almoco,4,35 122 | 11.69,2.31,Masculino,nao,qui,almoco,2,36 123 | 13.42,1.68,Feminino,nao,qui,almoco,2,33 124 | 14.26,2.5,Masculino,nao,qui,almoco,2,36 125 | 15.95,2.0,Masculino,nao,qui,almoco,2,35 126 | 12.48,2.52,Feminino,nao,qui,almoco,2,37 127 | 29.8,4.2,Feminino,nao,qui,almoco,6,39 128 | 8.52,1.48,Masculino,nao,qui,almoco,2,37 129 | 14.52,2.0,Feminino,nao,qui,almoco,2,52 130 | 11.38,2.0,Feminino,nao,qui,almoco,2,36 131 | 22.82,2.18,Masculino,nao,qui,almoco,3,46 132 | 19.08,1.5,Masculino,nao,qui,almoco,2,36 133 | 20.27,2.83,Feminino,nao,qui,almoco,2,40 134 | 11.17,1.5,Feminino,nao,qui,almoco,2,38 135 | 12.26,2.0,Feminino,nao,qui,almoco,2,43 136 | 18.26,3.25,Feminino,nao,qui,almoco,2,49 137 | 8.51,1.25,Feminino,nao,qui,almoco,2,33 138 | 10.33,2.0,Feminino,nao,qui,almoco,2,37 139 | 14.15,2.0,Feminino,nao,qui,almoco,2,35 140 | 16.0,2.0,Masculino,sim,qui,almoco,2,34 141 | 13.16,2.75,Feminino,nao,qui,almoco,2,47 142 | 17.47,3.5,Feminino,nao,qui,almoco,2,41 143 | 34.3,6.7,Masculino,nao,qui,almoco,6,41 144 | 41.19,5.0,Masculino,nao,qui,almoco,5,41 145 | 27.05,5.0,Feminino,nao,qui,almoco,6,31 146 | 16.43,2.3,Feminino,nao,qui,almoco,2,36 147 | 8.35,1.5,Feminino,nao,qui,almoco,2,48 148 | 18.64,1.36,Feminino,nao,qui,almoco,3,38 149 | 11.87,1.63,Feminino,nao,qui,almoco,2,49 150 | 9.78,1.73,Masculino,nao,qui,almoco,2,50 151 | 7.51,2.0,Masculino,nao,qui,almoco,2,36 152 | 14.07,2.5,Masculino,nao,dom,jantar,2,47 153 | 13.13,2.0,Masculino,nao,dom,jantar,2,47 154 | 17.26,2.74,Masculino,nao,dom,jantar,3,30 155 | 24.55,2.0,Masculino,nao,dom,jantar,4,33 156 | 19.77,2.0,Masculino,nao,dom,jantar,4,39 157 | 29.85,5.14,Feminino,nao,dom,jantar,5,38 158 | 48.17,5.0,Masculino,nao,dom,jantar,6,42 159 | 25.0,3.75,Feminino,nao,dom,jantar,4,47 160 | 13.39,2.61,Feminino,nao,dom,jantar,2,40 161 | 16.49,2.0,Masculino,nao,dom,jantar,4,36 162 | 21.5,3.5,Masculino,nao,dom,jantar,4,43 163 | 12.66,2.5,Masculino,nao,dom,jantar,2,35 164 | 16.21,2.0,Feminino,nao,dom,jantar,3,46 165 | 13.81,2.0,Masculino,nao,dom,jantar,2,38 166 | 17.51,3.0,Feminino,sim,dom,jantar,2,39 167 | 24.52,3.48,Masculino,nao,dom,jantar,3,38 168 | 20.76,2.24,Masculino,nao,dom,jantar,2,42 169 | 31.71,4.5,Masculino,nao,dom,jantar,4,43 170 | 10.59,1.61,Feminino,sim,sab,jantar,2,39 171 | 10.63,2.0,Feminino,sim,sab,jantar,2,29 172 | 50.81,10.0,Masculino,sim,sab,jantar,3,46 173 | 15.81,3.16,Masculino,sim,sab,jantar,2,40 174 | 7.25,5.15,Masculino,sim,dom,jantar,2,37 175 | 31.85,3.18,Masculino,sim,dom,jantar,2,48 176 | 16.82,4.0,Masculino,sim,dom,jantar,2,32 177 | 32.9,3.11,Masculino,sim,dom,jantar,2,37 178 | 17.89,2.0,Masculino,sim,dom,jantar,2,37 179 | 14.48,2.0,Masculino,sim,dom,jantar,2,47 180 | 9.6,4.0,Feminino,sim,dom,jantar,2,36 181 | 34.63,3.55,Masculino,sim,dom,jantar,2,39 182 | 34.65,3.68,Masculino,sim,dom,jantar,4,49 183 | 23.33,5.65,Masculino,sim,dom,jantar,2,26 184 | 45.35,3.5,Masculino,sim,dom,jantar,3,38 185 | 23.17,6.5,Masculino,sim,dom,jantar,4,37 186 | 40.55,3.0,Masculino,sim,dom,jantar,2,41 187 | 20.69,5.0,Masculino,nao,dom,jantar,5,50 188 | 20.9,3.5,Feminino,sim,dom,jantar,3,34 189 | 30.46,2.0,Masculino,sim,dom,jantar,5,42 190 | 18.15,3.5,Feminino,sim,dom,jantar,3,44 191 | 23.1,4.0,Masculino,sim,dom,jantar,3,37 192 | 15.69,1.5,Masculino,sim,dom,jantar,2,41 193 | 19.81,4.19,Feminino,sim,qui,almoco,2,32 194 | 28.44,2.56,Masculino,sim,qui,almoco,2,47 195 | 15.48,2.02,Masculino,sim,qui,almoco,2,41 196 | 16.58,4.0,Masculino,sim,qui,almoco,2,34 197 | 7.56,1.44,Masculino,nao,qui,almoco,2,38 198 | 10.34,2.0,Masculino,sim,qui,almoco,2,44 199 | 43.11,5.0,Feminino,sim,qui,almoco,4,39 200 | 13.0,2.0,Feminino,sim,qui,almoco,2,40 201 | 13.51,2.0,Masculino,sim,qui,almoco,2,40 202 | 18.71,4.0,Masculino,sim,qui,almoco,3,48 203 | 12.74,2.01,Feminino,sim,qui,almoco,2,49 204 | 13.0,2.0,Feminino,sim,qui,almoco,2,43 205 | 16.4,2.5,Feminino,sim,qui,almoco,2,53 206 | 20.53,4.0,Masculino,sim,qui,almoco,4,45 207 | 16.47,3.23,Feminino,sim,qui,almoco,3,43 208 | 26.59,3.41,Masculino,sim,sab,jantar,3,36 209 | 38.73,3.0,Masculino,sim,sab,jantar,4,45 210 | 24.27,2.03,Masculino,sim,sab,jantar,2,35 211 | 12.76,2.23,Feminino,sim,sab,jantar,2,45 212 | 30.06,2.0,Masculino,sim,sab,jantar,3,37 213 | 25.89,5.16,Masculino,sim,sab,jantar,4,47 214 | 48.33,9.0,Masculino,nao,sab,jantar,4,36 215 | 13.27,2.5,Feminino,sim,sab,jantar,2,46 216 | 28.17,6.5,Feminino,sim,sab,jantar,3,38 217 | 12.9,1.1,Feminino,sim,sab,jantar,2,38 218 | 28.15,3.0,Masculino,sim,sab,jantar,5,49 219 | 11.59,1.5,Masculino,sim,sab,jantar,2,42 220 | 7.74,1.44,Masculino,sim,sab,jantar,2,39 221 | 30.14,3.09,Feminino,sim,sab,jantar,4,41 222 | 12.16,2.2,Masculino,sim,sex,almoco,2,49 223 | 13.42,3.48,Feminino,sim,sex,almoco,2,41 224 | 8.58,1.92,Masculino,sim,sex,almoco,1,36 225 | 15.98,3.0,Feminino,nao,sex,almoco,3,40 226 | 13.42,1.58,Masculino,sim,sex,almoco,2,45 227 | 16.27,2.5,Feminino,sim,sex,almoco,2,50 228 | 10.09,2.0,Feminino,sim,sex,almoco,2,51 229 | 20.45,3.0,Masculino,nao,sab,jantar,4,38 230 | 13.28,2.72,Masculino,nao,sab,jantar,2,37 231 | 22.12,2.88,Feminino,sim,sab,jantar,2,42 232 | 24.01,2.0,Masculino,sim,sab,jantar,4,45 233 | 15.69,3.0,Masculino,sim,sab,jantar,3,39 234 | 11.61,3.39,Masculino,nao,sab,jantar,2,42 235 | 10.77,1.47,Masculino,nao,sab,jantar,2,35 236 | 15.53,3.0,Masculino,sim,sab,jantar,2,52 237 | 10.07,1.25,Masculino,nao,sab,jantar,2,30 238 | 12.6,1.0,Masculino,sim,sab,jantar,2,50 239 | 32.83,1.17,Masculino,sim,sab,jantar,2,38 240 | 35.83,4.67,Feminino,nao,sab,jantar,3,42 241 | 29.03,5.92,Masculino,nao,sab,jantar,3,40 242 | 27.18,2.0,Feminino,sim,sab,jantar,2,35 243 | 22.67,2.0,Masculino,sim,sab,jantar,2,30 244 | 17.82,1.75,Masculino,nao,sab,jantar,2,38 245 | 18.78,3.0,Feminino,nao,qui,jantar,2,40 246 | -------------------------------------------------------------------------------- /Oficinas/oficina_introdução_nlp/1-Relembrando-Python-Com_Respostas.ipynb: -------------------------------------------------------------------------------- 1 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"1-Relembrando-Python-Com_Respostas.ipynb","version":"0.3.2","provenance":[]},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"GMfLuG1IXua6","colab_type":"text"},"source":["# Relembrando Python"]},{"cell_type":"markdown","metadata":{"id":"jd5kvgyRXua8","colab_type":"text"},"source":["# Variaveis\n","\n","Podemos ter diversos tipos de variaveis em Python, as que vamos mais utilizar serão as strings e números."]},{"cell_type":"code","metadata":{"id":"UloPIIy2Xua-","colab_type":"code","colab":{}},"source":["## Números\n","\n","meuint = 7\n","print(meuint)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"38JbqsktXubD","colab_type":"code","colab":{}},"source":["meufloat = 7.0\n","print(meufloat)\n","\n","# Podemos também converter inteiros para float\n","meufloat2 = float(9)\n","print(meufloat2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"jTWQwtpIXubH","colab_type":"code","colab":{}},"source":["## Strings\n","\n","# Podemos usar tanto aspas simples quanto aspas duplas\n","minhastring = 'ao infinito e alem'\n","print(minhastring)\n","\n","minhastring2 = \"ao infinito e alem\"\n","print(minhastring2)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FLMy_5ZqXubN","colab_type":"code","colab":{}},"source":["# Podemos somar numeros...\n","valorA = 1\n","valorB = 2\n","valorC = valorA + valorB\n","print(\"Resultado: {}\\n\".format(valorC))\n","\n","# e concatenar strings\n","tema = \"Intro NLP\"\n","data = \"18/05/2019\"\n","curso = \"Curso de \" + tema + \" em \" + data\n","print(curso)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"N5YqJucVXubS","colab_type":"code","colab":{}},"source":["# Acessando Valores em Strings\n","\n","var1 = 'Pyladies'\n","var2 = \"Ciência de Dados\"\n","\n","print(\"var1[0]: \", var1[0])\n","print(\"var2[1:5]: \", var2[1:5])"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Pc34w5OIXubV","colab_type":"text"},"source":["# Listas\n","\n","Uma lista no Python armazena valores separados por vírgulas. No nosso caso, esses valores serão strings e possíveis números."]},{"cell_type":"code","metadata":{"id":"OU_7aWQBXubY","colab_type":"code","colab":{}},"source":["minhalista = [\"a\",\"b\",\"c\"]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"e-1zch6dXubb","colab_type":"code","colab":{}},"source":["minhalista"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"evZ-Q-vqXube","colab_type":"code","colab":{}},"source":["minhalista2 = [1,2,3,4,5]\n","minhalista2"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"PauNu2xqXubh","colab_type":"text"},"source":["Cada item da lista possui um índice (ou posição). \n","\n","Usando um índice de lista, você pode recuperar o item individual.\n","\n","Atenção: Em Python os índices começam em 0!"]},{"cell_type":"code","metadata":{"id":"PzgVFD9vXubi","colab_type":"code","colab":{}},"source":["minhalista[0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"9xEcseHMXubp","colab_type":"code","colab":{}},"source":["minhalista2[0]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2UfMZxs-Xubt","colab_type":"text"},"source":["Também podemos usar um intervalo de índices para chamar um intervalo da lista."]},{"cell_type":"code","metadata":{"id":"VEZLAa-UXubu","colab_type":"code","colab":{}},"source":["minhalista2[0:2]"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"7QEDG1x5Xubw","colab_type":"text"},"source":["O primeiro número nos diz onde começar, enquanto o segundo nos diz onde terminar e é exclusivo.\n","\n","Se não inserirmos o primeiro número, receberemos os primeiros x itens, onde x é o segundo número de índice que fornecemos."]},{"cell_type":"code","metadata":{"id":"woPYrOazXubx","colab_type":"code","colab":{}},"source":["minhalista[:2]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hUdDrRIqXub0","colab_type":"code","colab":{}},"source":["minhalista2[:3]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"k0Q8iJr6Xub2","colab_type":"code","colab":{}},"source":["minhalista2[-2:] # Retorna os ultimos 2 numeros"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QtB7o02SXub6","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"5eef0195-024d-46e6-f0cd-38595d417913","executionInfo":{"status":"ok","timestamp":1558181920584,"user_tz":180,"elapsed":884,"user":{"displayName":"Juliana Neves","photoUrl":"https://lh5.googleusercontent.com/-8tAstheGj8I/AAAAAAAAAAI/AAAAAAAAzyY/-HJ0sDR0-1E/s64/photo.jpg","userId":"09752424162683127104"}}},"source":["# Exercicio\n","# Crie uma lista com 10 números e adicione somente somente os 2 ultimos a uma nova lista\n","minhaLista = [1,2,3,4,5,6,7,8,9,10]\n","\n","minhaLista2 = minhaLista[-2:]\n","\n","minhaLista2"],"execution_count":1,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[9, 10]"]},"metadata":{"tags":[]},"execution_count":1}]},{"cell_type":"markdown","metadata":{"id":"rbgHhpYCXub-","colab_type":"text"},"source":["# Expressão Regular - Regex\n","\n","Expressões regulares são usadas para identificar se um padrão existe em uma determinada seqüência de caracteres (string) ou não.\n","\n","Para visualizar o que o modeulo re está executando, tente utilizar o https://regex101.com/ ou https://regexr.com/\n"]},{"cell_type":"markdown","metadata":{"id":"yC9E4iZxXub_","colab_type":"text"},"source":["### O módulo re oferece um conjunto de funções que nos permite pesquisar uma string por uma correspondência:"]},{"cell_type":"code","metadata":{"id":"yH4FI0ZIXucA","colab_type":"code","colab":{}},"source":["# import da função re — Regular expression operations \n","import re"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UQq_yD3XXucD","colab_type":"text"},"source":["Eles ajudam na manipulação de dados textuais, o que geralmente é um pré-requisito para projetos de ciência de dados que envolvem mineração de texto ou NLP. Existem algumas funções que nos auxiliam no tratamento dos dados, sendo elas:"]},{"cell_type":"markdown","metadata":{"id":"NiHe336rXucD","colab_type":"text"},"source":["### A função findall retorna uma lista que contém todos os matches."]},{"cell_type":"code","metadata":{"id":"7jPwvYSnXucE","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","x = re.findall(\"Ladies\", texto)\n","print(x)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"VuMCJq0RXucM","colab_type":"text"},"source":["Agora vamos aplicar em uma condição:"]},{"cell_type":"code","metadata":{"id":"GeYOf5E-XucN","colab_type":"code","colab":{}},"source":["padrao = r\"Cookies\"\n","sequencia = \"Queria uns Cookies\"\n","\n","if re.findall(padrao, sequencia):\n"," print(\"Encontrou!\")\n","else: \n"," print(\"Nao Encontrou :(\")"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JRwaFVqJXucQ","colab_type":"text"},"source":["Parabéns ele encontrou a palavra Cookie!\n","\n","Se colocarmos ela em minusculo, será que também irá encontrar?\n","\n","Faça o teste e explique o por que funciona ou não."]},{"cell_type":"code","metadata":{"id":"lEwUhY_IXucR","colab_type":"code","colab":{}},"source":["# Seu teste aqui:\n","\n","# Quais soluçoes podemos usar para corrigir esse problema?\n","\n","# jogar tudo para Upper ou lower case --- REMOVER ISSO"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"pJzq7l9XXucU","colab_type":"text"},"source":["### A função search retorna um objeto se houver uma correspondência em qualquer lugar da string."]},{"cell_type":"code","metadata":{"id":"uJfDAazVXucV","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","\n","# \"\\s\" encontra espaços em branco\n","x = re.search(\"\\s\", texto)\n","\n","print(\"O primeiro caracter com espaço em branco está na posição:\", x.start())"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_9HrEnxgXucY","colab_type":"text"},"source":["Um ponto (.) corresponde a qualquer caractere único - A função group () retorna a string encontrada pelo re"]},{"cell_type":"code","metadata":{"id":"I4ZyhKrCXucY","colab_type":"code","colab":{}},"source":["re.search(r'Co.k.e', 'Cookie').group()\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Cq9LeUCqXucd","colab_type":"text"},"source":["Usando o padrão \\w - Corresponde a qualquer letra minúsculas (\\w)."]},{"cell_type":"code","metadata":{"id":"L2Q9T2lSXuce","colab_type":"code","colab":{}},"source":["re.search(r'Co\\wk\\we', 'Cookie').group()\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"UOKN3m2mXucg","colab_type":"text"},"source":["Usando o padrão \\W (w maiúsculo) - Corresponde a qualquer caractere que não seja parte de \\w (w maísculo)."]},{"cell_type":"code","metadata":{"id":"ZweRNC8QXucg","colab_type":"code","colab":{}},"source":["\n","re.search(r'C\\Wke', 'C@ke').group()"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MJETxOihXucj","colab_type":"text"},"source":["A função span () retorna uma tupla contendo as posições inicial e final da correspondência."]},{"cell_type":"code","metadata":{"id":"osxhzAgUXucm","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","x = re.search(r\"\\bS\\w+\", texto)\n","print(x.span())"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Dcj3z-BEXucp","colab_type":"text"},"source":["A função split () retorna uma lista onde a string foi dividida em cada match:"]},{"cell_type":"code","metadata":{"id":"Sb6-_ym6Xucr","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","x = re.split(\"\\s\", texto)\n","print(x)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uJ_4WDEDXucv","colab_type":"text"},"source":["Podemos determinar a quantidade máxima de split:"]},{"cell_type":"code","metadata":{"id":"PO_-ErIfXucw","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","x = re.split(\"\\s\", texto, 1)\n","print(x)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BBsbkgTXXucz","colab_type":"text"},"source":["A função sub () substituí uma ou mais match por uma determinada string:"]},{"cell_type":"code","metadata":{"id":"PZPzDhCwXucz","colab_type":"code","colab":{}},"source":["texto = \"PyLadies SP faz parte da comunidade PyLadies Brasil\"\n","x = re.sub(\"\\s\", \"X \", texto)\n","print(x)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Hd_G05z2Xuc2","colab_type":"text"},"source":["Podemos usar para remover números de um texto:"]},{"cell_type":"code","metadata":{"id":"5fQUbcMNXuc2","colab_type":"code","colab":{}},"source":["texto = 'Rua Faustina 152'\n","\n","texto = re.sub('[-|0-9]',' ', texto)\n","print(texto)\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"2dHe49kiXuc5","colab_type":"text"},"source":["# List comprehension\n"]},{"cell_type":"markdown","metadata":{"id":"B0x4FDMpXuc5","colab_type":"text"},"source":["É uma estrutura importantíssima para se trabalhar com grandes conjuntos de dados, tendo uma performace superior a outras estruturas em python e simplifica a escrita do código."]},{"cell_type":"code","metadata":{"id":"gVP07NIEXuc6","colab_type":"code","colab":{}},"source":["# Retorna cada caracter em uma sequência de caracteres\n","\n","lst = [x for x in 'PyLadies']\n","lst"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"MwTHctaPXuc9","colab_type":"text"},"source":["A leitura da List Comprehension acime é basicamente a seguinte:\n","\n","Para cada caracter (x) em PyLadies, retorne o item (x)"]},{"cell_type":"code","metadata":{"id":"eMkAGTr8Xuc-","colab_type":"code","colab":{}},"source":["# Vamos confirmar se realmente é uma lista\n","\n","type(lst)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"QSyavncuXudB","colab_type":"code","colab":{}},"source":["# Raiz quadrada de um range de números\n","\n","raiz = [x**2 for x in range(0,6)]\n","raiz"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7pWx9BTBXudC","colab_type":"code","colab":{}},"source":["# Verificando se é um número par \n","\n","par = [x for x in range(13) if x % 2 == 0]\n","par\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"EArE6pjpXudE","colab_type":"code","colab":{}},"source":["# Operações aninhadas, ou seja, podemos por uma list comprehensions dentro da outra\n","\n","raiz = [x**2 for x in [x**2 for x in range(6)]]\n","raiz"],"execution_count":0,"outputs":[]}]} -------------------------------------------------------------------------------- /Oficinas/oficina_introdução_nlp/PYLADIES -NLP.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Oficinas/oficina_introdução_nlp/PYLADIES -NLP.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Materiais do Grupo de Estudos em Data Science GEDS - PyLadies SP 2 | 3 | ### Apresentações 4 | [Documento de Apresentação do GEDS](https://github.com/PyLadiesSP/data-science/blob/master/Apresentacoes/2019_Capyra/grupo_de_estudos_estimula_troca_de_saber_Pyladies_SP.pdf) 5 | 6 | ### Links uteis 7 | Alguns links úteis com [bases de dados](https://github.com/PyLadiesSP/data-science/tree/master/Links_uteis/Bases_de_dados) para serem analisadas e mais variados [textos](https://github.com/PyLadiesSP/data-science/tree/master/Links_uteis/Textos_sobre_Ciencia_de_Dados) sobre Ciência de Dados 8 | 9 | ### Atividades 10 | Pasta com arquivos utilizados em Palestras e Oficinas de Ciência de Dados e Python 11 | 12 | #### Oficina Introdução a Estatística e Pandas 13 | - [Material](https://github.com/PyLadiesSP/data-science/blob/master/Oficinas/oficina_introdu%C3%A7%C3%A3o_estatistica_pandas/Workshop%20Introdu%C3%A7%C3%A3o%20a%20Estat%C3%ADstica%20e%20Pandas%20Respostas.pdf) 14 | - [Jupyter notebook utilizado](https://github.com/PyLadiesSP/data-science/blob/master/Oficinas/oficina_introdu%C3%A7%C3%A3o_estatistica_pandas/tips_notebook.ipynb) 15 | - [Dados utilizados](https://github.com/PyLadiesSP/data-science/blob/master/Oficinas/oficina_introdu%C3%A7%C3%A3o_estatistica_pandas/tips.csv) 16 | 17 | #### Palestra Python Brasil 2021 18 | - [Material - 7 passos do projeto de Ciência de Dados - em pdf](https://github.com/PyLadiesSP/data-science/blob/master/Apresentacoes/2021_Python_Brasil/Roteiro_7passos.pdf) 19 | 20 | ### Trilha de Estudos 21 | Material utilizado pelo grupo de estudos, com documentos separados por cada tópico estudado. 22 | 23 | - [Proposta de Estudo](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/proposta_estudo.pdf) 24 | 25 | - [Probabilidade básica - Introdução + Exercícios](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/01%20material.pdf) 26 | 27 | - [Probabilidade básica - Propriedades](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/02%20material.pdf) 28 | 29 | - [Probabilidade básica - Condicional](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/03%20material.pdf) 30 | 31 | - [Probabilidade básica - Teorema de Bayes](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/04%20material.pdf) 32 | 33 | - [Introdução a Variáveis Aleatórias](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/05%20material.pdf) 34 | 35 | - [Introdução a Variáveis Aleatórias Contínuas - Distribuição Normal](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/06%20material.pdf) 36 | 37 | - [Introdução à Amostragem e Inferência Estatística](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/07%20material.pdf) 38 | 39 | - [Teste de Hipótese I](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/08%20material.pdf) 40 | 41 | - [Teste de Hipótese II](https://github.com/PyLadiesSP/data-science/blob/master/Trilha_de_Estudos/09%20material.pdf) 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /Trilha_de_Estudos/01 material.pdf: 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matplotlib.pyplot as plt\n","import pandas as pd\n","from google.colab import files\n","uploaded = files.upload()"],"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/html":["\n"," \n"," \n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. 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42459.0361.0Femininonaodomjantar434
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"],"text/plain":[" total_conta gorjeta genero ... horario pessoas_mesa tempo_permanencia\n","0 1699.0 101.0 Feminino ... jantar 2 41\n","1 1034.0 166.0 Masculino ... jantar 3 40\n","2 2101.0 3.5 Masculino ... jantar 3 49\n","3 2368.0 331.0 Masculino ... jantar 2 43\n","4 2459.0 361.0 Feminino ... jantar 4 34\n","\n","[5 rows x 8 columns]"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"BglydWVKdQGB","colab_type":"code","outputId":"5127e110-fcd9-4411-d992-b59b9a792637","executionInfo":{"status":"ok","timestamp":1566345337093,"user_tz":180,"elapsed":674,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":253}},"source":["tips.info()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["\n","RangeIndex: 244 entries, 0 to 243\n","Data columns (total 8 columns):\n","total_conta 244 non-null float64\n","gorjeta 244 non-null float64\n","genero 244 non-null object\n","fumante 244 non-null object\n","dia 244 non-null object\n","horario 244 non-null object\n","pessoas_mesa 244 non-null int64\n","tempo_permanencia 244 non-null int64\n","dtypes: float64(2), int64(2), object(4)\n","memory usage: 15.3+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"mjpVrC0ndy4Z","colab_type":"text"},"source":["**Qual o tempo de permanência médio que os clientes ficam no restaurante?**"]},{"cell_type":"code","metadata":{"id":"Gp7oFzPecd3I","colab_type":"code","outputId":"1368ac03-ae96-430f-fc5a-fa3753463dcc","executionInfo":{"status":"ok","timestamp":1566434445561,"user_tz":180,"elapsed":626,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["media = tips['tempo_permanencia'].mean()\n","print(\"A média de tempo é de\", round(media, 2), \"min\")"],"execution_count":11,"outputs":[{"output_type":"stream","text":["A média de tempo é de 40.26 min\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"f09J65EDd2-D","colab_type":"text"},"source":["**Qual a variabilidade desse tempo?**"]},{"cell_type":"code","metadata":{"id":"bneCyGE3ckZg","colab_type":"code","outputId":"e780ce4b-fe06-4d1c-faf3-4b04705884d7","executionInfo":{"status":"ok","timestamp":1566345187833,"user_tz":180,"elapsed":529,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":300}},"source":["tips.describe()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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total_contagorjetapessoas_mesatempo_permanencia
count244.000000244.000000244.000000244.000000
mean1795.606557137.9385252.56967240.262295
std999.650888168.0466750.9511005.157285
min9.6000001.0000001.00000026.000000
25%1212.7500002.5750002.00000037.000000
50%1687.5000005.1000002.00000040.000000
75%2277.500000265.7500003.00000044.000000
max5081.000000758.0000006.00000053.000000
\n","
"],"text/plain":[" total_conta gorjeta pessoas_mesa tempo_permanencia\n","count 244.000000 244.000000 244.000000 244.000000\n","mean 1795.606557 137.938525 2.569672 40.262295\n","std 999.650888 168.046675 0.951100 5.157285\n","min 9.600000 1.000000 1.000000 26.000000\n","25% 1212.750000 2.575000 2.000000 37.000000\n","50% 1687.500000 5.100000 2.000000 40.000000\n","75% 2277.500000 265.750000 3.000000 44.000000\n","max 5081.000000 758.000000 6.000000 53.000000"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"markdown","metadata":{"id":"5qenaaeUdqaq","colab_type":"text"},"source":["**Plotar um histograma dessa variável e discutir sobre o formato.**"]},{"cell_type":"code","metadata":{"id":"Ke-e2JSmcs8x","colab_type":"code","outputId":"a3c28348-751f-473f-99ef-53d050d63359","executionInfo":{"status":"ok","timestamp":1566433913162,"user_tz":180,"elapsed":627,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":296}},"source":["plt.hist(tips['tempo_permanencia'], bins=5)\n","plt.xlabel('Tempo de permanência')\n","plt.ylabel('Frequência')\n","plt.title('Histograma de tempo de permanência de clientes em restaurante')\n","plt.show()"],"execution_count":9,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaYAAAEXCAYAAADm5+DTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xm4XFWZ7/HvLwMkQBhCYoQECALi\nRRSECLHRdkAURSXaNOIYAUWuyKCiBBub0Eo33IfxXrtBEDUMzSCz4gBE0HZgSCAQBrlMARICBEyA\nCE0IefuPtUp2iqpzqnJOnb3rnN/nec5z9rzftdeu9dYeam9FBGZmZlUxrOwAzMzMipyYzMysUpyY\nzMysUpyYzMysUpyYzMysUpyYzMysUgYkMUm6W9J7BmJd3ULSTyR9r+w4BpKkyZJC0oiyYxlokoZL\nul7SjZJG9sPyNpe0XNLwflhWR+olL3Pr3H2mpO/05/Jt8OpzYpK0QNL764Z9QdLva/0R8eaIuLGX\n5QzZRqtdxQ+8dY1jgFnAfwD/0teFRcSjEbFeRLzS58gGQEQcHBHf7etyJL1H0sL+iKkbVb2d7K/4\nKlm4TpA0IiJWlh2Hla+MfSEijiv0XjKQ67ZqqXJbVJnYIqJPf8AC4P11w74A/L7RNMAuwBzgOeBJ\n4JQ8/FEggOX57x2kI7pjgEeAp4BzgQ0Ky/18HvcM8J269cwELgXOz+v6Yl73n4BlwGLg+8BaheUF\n8BXgfuB54LvAVsAf8zIuqU0PbAT8HFgCLM3dk3rYTm8DbsvLvRi4CPheYfxHgHk5tj8Cb22ynN/l\nOP+at9Mne5s/b5dvAnfm+c4BJgC/zPFcD2yUp52cl38Q8HjeTkcWlrU2cFoe93juXrtJrMOBk4Cn\ngYeAQ/KyR+TxG+RYFgOLgO8Bw5ssq1afF+eYbwN2KIzfFLgs18fDwGEN5i3uCzOBn+ZhzwPzgTcC\nR5P2tceADxSWsT9wb572IeDLhXHvARYC38jzLgb2r9tmJ5H28SeBM4HRLc47GjiZtJ8/C/w+D6vV\n04je4utwvQwHvg08mNc9F9is8HnaOnf/hBb3d9L+eiRpf3021/koYF3gRWAVr7YTm5LaiRk5hmdI\nn9OxeVmjch0/k9d1KzChSVl624da3l+atJNH5TK9RDoo6Gl97bSTWwG/yWV8GrgA2LCuXdu60P+3\nuuDV/e8o4AngPHpp24AbSW3jH/K2uBYY1yy+PPwA0v65FPg1sEWPeaWExPQn4HO5ez1gal2DOKIw\n3wHAA8Ab8rSXA+flcdvlgr8TWIv0QXuZ1RPTy8A00o47GtgZmJp3isl5Qx1RV4FXAesDb8470Oy8\n/g2Ae4DpedqNgX8A1gHGkHbaK5tso7VIDcvXgJHAPjm22s7xNtLOvSvpgz49b7NmDX79jtbj/Ln7\nJlIympinvS3PN4q0Ux9bVw8XkhqCt5B20Np2/Ze8rNcB40mNynebxHkw8GdgM2AscAOrN4BXAD/I\n63kdcAtNGtRCfe6Tt+GRpA/zyFy/c4F/ztv6DaQG94M97Aszgf8GPpj3h3Pz8v4pL/NLwMOF9e9F\nagAEvBt4Adip8OFembfNSODDeXwt2Z8KXJ23wRjgZ8C/tTjvv5Magom5bv+OlOhq9TSit/g6XC/f\nJDXS2+Z17wBs3FNiorX99RZSwz2W9Dk9uNiQ1sVwOGmfnJS3zQ+AC/O4L+ftvU5e187A+g3K0co+\n1PL+0qSdnJe3+egW1tdOO7k1sEcu+3jSl9fT2khMK4ET8/yj6aVtI+2PD5IS8+jcf0IP8e1Nasf/\nV952xwB/HIjEtJz0baT29wLNE9PvgOPIGbYwTaMCzQa+UujfltTAjMgVemFh3DrAClZPTL/rJfYj\ngCvqKnC3Qv9c4KhC/8nFCq9b1o7A0ibj/p50dKHCsD8Wdo4zqGvcgfuAdzdZXv2O1uP8eft/pjDu\nMuCMQv+htR2vUA9vKoz/P8A5uftB4MOFcR8EFjSJ8zfkBiX3f6BWx6Qk+RL5yCGP/xRwQ5NlzQRu\nqmtIFgPvIjVwj9ZNfzTw42b7Qh52XaH/o6T9eHjuH5Nj3bBJPFcChxc+3C/W7btPkb4EiXSUulVh\n3DvIjVgv8w7L43ZosP5aPY3oLb4O18t9wN697aes3hi2sr9+tm7/O7OwveoT073A7oX+TXi1nTiA\nHs5AFOZpZR/qy/6yADigjfW13E42WNc04PZG9dCgLt5DajdH9bC81do2UiI6ptD/FeBXzeIjnZk5\nsO6z+wI9HDX11zWmaRFxfa1H0hdIp0saOZD07fDPkh4GjouInzeZdlPSkUbNI7z64dmUdPgMQES8\nIOmZuvkfK/ZIeiNwCjCFlMhGkJJP0ZOF7hcb9L8+L2sd0jfhPUmHvgBjJA2P116Q3hRYFLlWCmWp\n2QKYLunQwrC18nytaGX+3sq1Xt0yi9vuEdKREzSuk2ZxrlZHvLbMI4HFkmrDhtVNX69Y36vyRfBN\nSR+ETSUtK0w7HPivJuWpqd8GTxfq7sX8fz1gmaQPAceSviUOI+0/8wvzPxOrn5t/Ic87Pk87t1BO\n5fh6m3cc6Yj2wQaxr6aF+Ir6s142ayW+Oq3sr08Uul+g58/CFsAVklYVhr1CaifOyzFeJGlD0qm4\nf4qIlxsso7d9qOX9pUmcxW3Y2/pabiclTQBOJ31JG0Oqr6VNYmhkSUT8d2F5rbRt9fVT334UbQGc\nLunkYtikswCPNJphwG9+iIj7gU9JGgZ8ArhU0sakxqXe46RC1WxOOux8kvRtedvaCEm1Q9DVVlfX\nfwZwO/CpiHhe0hGkU0Nr4ht5/btGxBOSdszLVoNpFwMTJamQnDbn1Q/0Y8DxEXH8GsbS1/kb2Yx0\nugdSrI/n7lqd3N1gXL3FeTkUpq15jPTNfFy0frH1b8vK+8+kvO6VpCOQbXqYt9H+1RJJa5OOMj8P\nXBURL0u6ksZ1Xe9pUqP15ohY1OaqnyadPtoKuKMf4+vPenksx3dXC9MW51nT/bVRPT5GOhr5Q5N5\njgOOkzQZ+AXp6OycBsvobR/qq2LsPa6vzXbyX/Pwt0TEXyRNI10/r3mB9EWl5vWk60qN4oL22rbX\nhN5gWK2+L2hhfqCEH9hK+qyk8RGxile/WawiXcdYRTrXWnMh8DVJW0paj1QBF+cPzKXARyX9naS1\nSIfavW24MaSLicslvQn4330oyhhSg7NM0ljSt9Vm/kRqPA+TNFLSJ0gXN2vOBg6WtKuSdSXtJWlM\nk+U9yerbqd35W/EdSetIejPpwvrFefiFwDGSxksaRzqlen6TZVxCKvMkSRuRLlADEBGLSRdNT5a0\nvqRhkraS9O4eYtpZ0ifyrahHkBrQm0jXI56XdJSk0fk3Q9tLensfyl+0Fun8+xJgZT46+UArM+b9\n/GzgVEmvA5A0UdIHW5z3R8ApkjbN5XpHTkR9ia8/6+WHwHclbZP3vbfmBrQnfdlfnwQ2lrRBYdiZ\nwPGStgDI++beufu9kt6i9Huv50in+FbVL5TO70Ntra/NdnIM6bTis5Imkq77Fc0DPp3XsSfpGmRP\n2mnb6jWK70zg6NyWIGkDSf/Y00LKePLDnsDdkpaTDj/3i4gXI+IF4HjgD5KWSZpK+lCeRzrf+jDp\n2+OhABFxd+6+iPQNcDnp3PxLPaz7SODTpDtJzubVxnZNnEa68Pc0qXH8VbMJI2IF6VvPF4C/AJ8k\n3chRGz+HdPH0+6RD8AfytM3MBGbl7bTvGszfit/m5cwGToqIa/Pw75HuFrqTdKrotjyskbNJd+Dc\nkae7vG7850mN6j057ktJ1weauYq07ZYCnwM+EREv59MLHyGdC3+YVCc/JN2w0mcR8TxwGKlBX0ra\nh65uYxFHkbblTZKeI90FuW3Ps/zNkaTtfCtp3zmRus/tGsTXn/VySl7vtaSG/xzS56KpvuyvEfFn\n0pejh/L+vympHbkauFbS86TP4655ltfn+J8jXYv6LalNqV9uR/ehNVhfO+3kccBOpDsYr+G19Xk4\n6ZrYMuAzpOuPPWm5bWtQrtfEFxFXkPbbi/L+fxfwoZ6Wo9Uve3SvfES1DNgmIh4uO55ulU93PAyM\nbOMUW8dJmkm6gPvZsmMxs87q6mflSfpoPt20Lul28fmku1/MzKxLdXViIt0fX/uh5zakw93BcQho\nZjZEDZpTeWZmNjh0+xGTmZkNMk5MZmZWKV3/dPFx48bF5MmTyw7DzKxrzJ079+mIGF92HM10fWKa\nPHkyc+bMKTsMM7OuIanho4CqwqfyzMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUpyY\nzMysUpyYzMysUrr+B7ZmvZk845qyQxhQC07Yq+wQzPrER0xmZlYpTkxmZlYpTkxmZlYpTkxmZlYp\nTkxmZlYpTkxmZlYpTkxmZlYpTkxmZlYpTkxmZlYpTkxmZlYpTkxmZlYpHU1Mkn4k6SlJdxWGjZV0\nnaT78/+N8nBJ+r+SHpB0p6SdOhmbmZlVU6ePmH4C7Fk3bAYwOyK2AWbnfoAPAdvkv4OAMzocm5mZ\nVVBHE1NE/A74S93gvYFZuXsWMK0w/NxIbgI2lLRJJ+MzM7PqKeMa04SIWJy7nwAm5O6JwGOF6Rbm\nYWZmNoSUevNDRAQQ7c4n6SBJcyTNWbJkSQciMzOzspSRmJ6snaLL/5/KwxcBmxWmm5SHvUZEnBUR\nUyJiyvjx4zsarJmZDawyEtPVwPTcPR24qjD88/nuvKnAs4VTfmZ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elLQJQP7/VHkRDj5d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\nZmZWKU5MZmZWKf8D81l6bQyHAn8AAAAASUVORK5CYII=\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"_o33AOoVdowI","colab_type":"text"},"source":["**Selecionando um cliente aleatório, qual a probabilidade que ele permaneça mais que 40min?**\n","\n"]},{"cell_type":"code","metadata":{"id":"bZLObMHZdG6Q","colab_type":"code","outputId":"ea7c05d5-6245-454e-9587-e0c93baac115","executionInfo":{"status":"ok","timestamp":1566345697146,"user_tz":180,"elapsed":667,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":72}},"source":["condicao=[tips['tempo_permanencia']<=40,tips['tempo_permanencia']>40]\n","rotulo=['permanencia < 40 min','permanencia > 40 min']\n","\n","tips['tempo_categ']=np.select(condicao,rotulo)\n","tips['tempo_categ'].value_counts()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["permanencia < 40 min 131\n","permanencia > 40 min 113\n","Name: tempo_categ, dtype: int64"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"2BzTNn_Fev2J","colab_type":"text"},"source":["1. Número de clientes do restaurante: 244\n","2. Número de clientes que permanecem mais que 40min: 113\n","\n"]},{"cell_type":"code","metadata":{"id":"W2yZclFcelbi","colab_type":"code","outputId":"d3ef8c1c-2588-41bc-b188-e0aa2c19cec3","executionInfo":{"status":"ok","timestamp":1566346904300,"user_tz":180,"elapsed":677,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["p = 113/244\n","print(\"A probabilidade é de\", round(p*100, 2), \"%\") "],"execution_count":0,"outputs":[{"output_type":"stream","text":["A probabilidade é de 46.31 %\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"pA0mHOT5gipn","colab_type":"text"},"source":["**Pensando na programação do dono do restaurante sobre a quantidade de pessoas que frequentam o estabelecimento, precisamos saber quanto tempo a maioria das pessoas ficam. Qual é o tempo que 95% dos clientes poderiam ter sua refeição tranquilamente?**"]},{"cell_type":"markdown","metadata":{"id":"RxGS0WLeiDUZ","colab_type":"text"},"source":["Na curva de Gauss dois desvios padrão desde a média representam cerca de 95% dos dados"]},{"cell_type":"code","metadata":{"id":"228KEoujiCS_","colab_type":"code","outputId":"cbe7d961-0dd2-4ab9-9f88-06319084f8c1","executionInfo":{"status":"ok","timestamp":1566346649580,"user_tz":180,"elapsed":685,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":296}},"source":["plt.hist(tips['tempo_permanencia'])\n","plt.xlabel('Tempo de permanência')\n","plt.ylabel('Frequência')\n","plt.title('Histograma de tempo de permanência de clientes em restaurante')\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAaAAAAEXCAYAAADr+ZCUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3XmYXVWZ7/HvLwMkQBgCIQIBCiHi\nxQGECLHRVlAQRSXaNOKAUVHkigrdooCNbWihG+7D+Fy7QRA1gjLILDgQIkg7ICQQZrxhCJIQkoCJ\nEKGBkPf+sdaBnUOdqnNSdWpVnfp9nqee2vN+1177rPfs4eytiMDMzGygjSgdgJmZDU9OQGZmVoQT\nkJmZFeEEZGZmRTgBmZlZEU5AZmZWRL8lIEn3SnpXfy2vE0j6oaQTS8cxkCR1SQpJo0rHMtAkjZR0\ng6SbJI3uh+VtI2mlpJH9sKy21Ete5g65+xxJ3+zP5VtnayoBSVog6T11wz4t6be1/oh4Q0Tc1Mty\nhm3j1KrqB9uGjOOBmcB/Af/W14VFxJ8jYoOIeKnPkQ2AiDg8Ir7d1+VIepekhf0R01A02NvJ/oxv\nUBZwbUkaFRGrSsdh5ZXYFyLihErvpQO5bhtcBnNbNKhii4he/4AFwHvqhn0a+G130wC7A3OAp4El\nwOl5+J+BAFbmv7eRjsKOBx4FlgI/AjaqLPdTedxTwDfr1jMDuAy4MK/rc3ndfwBWAIuB7wDrVJYX\nwBeB+cAzwLeB7YHf52VcWpse2AS4FlgGLM/dk3rYTm8Bbs/LvQS4GDixMv4DwLwc2++BNzdYzs05\nzr/l7fTR3ubP2+VrwF15vvOBicAvcjw3AJvkabvy8g8DHs/b6ejKstYFzszjHs/d6zaIdSRwKvAk\n8DBwRF72qDx+oxzLYmARcCIwssGyavV5SY75dmDnyvgtgctzfTwCfKWbeav7wgzgp3nYM8DdwOuA\n40j72mPAvpVlfAa4P0/7MPCFyrh3AQuBr+Z5FwOfqdtmp5L28SXAOcDYJucdC5xG2s//Cvw2D6vV\n06je4mtzvYwEvgE8lNc9F9i68nnaIXf/kCb3d9L+ejRpf/1rrvMxwPrAc8BqXmkntiS1E8fmGJ4i\nfU7H52WNyXX8VF7XbcDEBmXpbR9qen9p0E4ek8v0POkLfk/ra6Wd3B74dS7jk8CPgY3r2rUdKv0v\n1wWv7H/HAE8AF9BL2wbcRGobf5e3xfXAZo3iy8M/S9o/lwO/ArbtNbe0KQH9ATgkd28ATK1r+EZV\n5vss8CDw2jztFcAFedxOuYBvB9YhfaBeZM0E9CIwjbSDjgV2A6bmyu/KG+Souoq6GtgQeEPeUWbn\n9W8E3AdMz9NuCvwDsB4wjrRzXtVgG61DakD+CRgNHJhjq+0EbyHtxHuQPtDT8zZr1LDX71A9zp+7\nbyElna3ytLfn+caQdt5v1dXDRaQP/JtIO2Jtu/5bXtbmwARS4/HtBnEeDjwAbA2MB25kzYbuSuC7\neT2bA7fSoOGs1OeBeRseTfrQjs71Oxf417ytX0tqWN/bw74wA/gf4L15f/hRXt6/5GV+Hniksv79\nSR90Ae8EngV2rXyIV+VtMxp4fx5fS+pnANfkbTAO+BnwH03O+5+kD/xWuW7/jpTQavU0qrf42lwv\nXyM1xjvmde8MbNpTAqK5/fVWUgM9nvQ5PbzaYNbFcCRpn5yUt813gYvyuC/k7b1eXtduwIbdlKOZ\nfajp/aVBOzkvb/OxTayvlXZyB2CfXPYJpC+pZ7aQgFYBp+T5x9JL20baHx8iJeCxuf/kHuI7gNSO\n/6+87Y4Hft+fCWgl6dtF7e9ZGiegm4ETyBmzMk13gc8Gvljp35HUkIzKFXdRZdx6wAusmYBu7iX2\no4Ar6ypqz0r/XOCYSv9p1YqtW9YuwPIG4/6edLSgyrDfV3aCs6lrxIE/Ae9ssLz6HarH+fP2/0Rl\n3OXA2ZX+L9d2sEo9vL4y/v8A5+fuh4D3V8a9F1jQIM5fkxuO3L9vrY5JyfB58pFAHv8x4MYGy5oB\n3FLXYCwG3kFqyP5cN/1xwA8a7Qt52KxK/wdJ+/HI3D8ux7pxg3iuAo6sfIifq9t3l5K+7Ih01Ll9\nZdzbyI1VL/OOyON27mb9tXoa1Vt8ba6XPwEH9Lafsmaj18z++sm6/e+cyvaqT0D3A++u9G/BK+3E\nZ+nhjEJlnmb2ob7sLwuAz7awvqbbyW7WNQ24o7t66KYu3kVqN8f0sLw12jZSwjm+0v9F4JeN4iOd\naTm07rP7LL0cBbVyDWhaRNxQ65H0adJpju4cSvq294CkR4ATIuLaBtNuSTpyqHmUVz4kW5IOewGI\niGclPVU3/2PVHkmvA04HppAS1ihSkqlaUul+rpv+1+RlrUf6Zrsf6ZAVYJykkfHqC8NbAosib/1K\nWWq2BaZL+nJl2Dp5vmY0M39v5dqgbpnVbfco6UgIuq+TRnGuUUe8usyjgcWSasNG1E1fr1rfq/PF\n6C1JO/yWklZUph0J/HeD8tTUb4MnK3X3XP6/AbBC0vuAb5G+9Y0g7T93V+Z/KtY8d/5snndCnnZu\npZzK8fU272akI9SHuol9DU3EV9Wf9bJ1M/HVaWZ/faLS/Sw9fxa2Ba6UtLoy7CVSO3FBjvFiSRuT\nTqH9S0S82M0yetuHmt5fGsRZ3Ya9ra/pdlLSROAs0pexcaT6Wt4ghu4si4j/qSyvmbatvn7q24+q\nbYGzJJ1WDZt0VP9o97O06SaEiJgPfEzSCOAjwGWSNiU1IvUeJwVfsw3pcHEJ6dvvjrURkmqHjmus\nrq7/bOAO4GMR8Yyko0indNbGV/P694iIJyTtkpetbqZdDGwlSZUktA2vfHAfA06KiJPWMpa+zt+d\nrUmnaSDF+njurtXJvd2Mq7c4L4fKtDWPkb5pbxbNX/R8eVl5/5mU172KdEQxuYd5u9u/miJpXdJR\n46eAqyPiRUlX0X1d13uS1Di9ISIWtbjqJ0mnfbYH7uzH+PqzXh7L8d3TxLTVedZ2f+2uHh8jHV38\nrsE8JwAnSOoCfk462jq/m2X0tg/1VTX2HtfXYjv573n4myLiL5Kmka5v1zxL+kJS8xrSdZ/u4oLW\n2rZXhd7NsFp9/7iJ+V/Wlh+iSvqkpAkRsZpXvimsJl1nWE06F1pzEfBPkraTtAFpQ1+SPxiXAR+U\n9HeS1iEdIve2gcaRLuqtlPR64H/3oSjjSA3LCknjSd8+G/kDqZH8iqTRkj5CushYcx5wuKQ9lKwv\naX9J4xosbwlrbqdW52/GNyWtJ+kNpAvcl+ThFwHHS5ogaTPSqdALGyzjUlKZJ0nahHShGICIWEy6\neHmapA0ljZC0vaR39hDTbpI+km/xPIrUUN5Cul7wjKRjJI3Nv7l5o6S39qH8VeuQzo8vA1blo419\nm5kx7+fnAWdI2hxA0laS3tvkvN8HTpe0ZS7X23LC6Ut8/Vkv3wO+LWly3vfenBvKnvRlf10CbCpp\no8qwc4CTJG0LkPfNA3L3XpLepPR7qadJp+ZW1y+U9u9DLa2vxXZyHOl04F8lbUW6Llc1D/h4Xsd+\npGuEPWmlbavXXXznAMfltgRJG0n6x94W1K4nIewH3CtpJemw8eCIeC4ingVOAn4naYWkqaQP3wWk\n86GPkL4NfhkgIu7N3ReTvtGtJJ07f76HdR8NfJx058Z5vNKoro0zSRfgniQ1gr9sNGFEvED6FvNp\n4C/AR0k3VNTGzyFdxPwO6dD5wTxtIzOAmXk7HbQW8zfjN3k5s4FTI+L6PPxE0t05d5FO8dyeh3Xn\nPNIdL3fm6a6oG/8pUuN5X477MtL5+0auJm275cAhwEci4sV8WuADpHPVj5Dq5HukG0f6LCKeAb5C\nariXk/aha1pYxDGkbXmLpKdJdx3u2PMsLzuatJ1vI+07p1D32VyL+PqzXk7P672e1MCfT/pcNNSX\n/TUiHiB9CXo47/9bktqRa4DrJT1D+jzukWd5TY7/adK1ot+Q2pT65bZ1H1qL9bXSTp4A7Eq6Y/A6\nXl2fR5KuWa0APkG6PtiTptu2bsr1qvgi4krSfntx3v/vAd7X27K05iWLwS0fIa0AJkfEI6XjGary\naYpHgNEtnBprO0kzSBdSP1k6FjNrv0H/LDhJH8ynidYn3YZ9N+luEzMzG8IGfQIi3V9e+0HkZNJh\n6tA5bDMzs24NqVNwZmbWOYbCEZCZmXUgJyAzMytiSDwNe7PNNouurq7SYZiZDSlz5859MiImlI6j\nkSGRgLq6upgzZ07pMMzMhhRJDR+DMxj4FJyZmRXhBGRmZkU4AZmZWRFOQGZmVoQTkJmZFeEEZGZm\nRTgBmZlZEU5AZmZWxJD4IapZb7qOva7YuhecvH+xdZsNZT4CMjOzIpyAzMysCCcgMzMrwgnIzMyK\ncAIyM7MinIDMzKwIJyAzMyvCCcjMzIpwAjIzsyKcgMzMrAgnIDMzK8IJyMzMinACMjOzIpyAzMys\nCL+OwayPSr0Kwq+BsKHOR0BmZlaEE5CZmRXhBGRmZkW0/RqQpAXAM8BLwKqImCJpPHAJ0AUsAA6K\niOXtjsXMzAaPgToC2isidomIKbn/WGB2REwGZud+MzMbRkqdgjsAmJm7ZwLTCsVhZmaFDEQCCuB6\nSXMlHZaHTYyIxbn7CWBi/UySDpM0R9KcZcuWDUCYZmY2kAbid0Bvj4hFkjYHZkl6oDoyIkJS1M8U\nEecC5wJMmTLlVePNzGxoa/sRUEQsyv+XAlcCuwNLJG0BkP8vbXc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VoH0adsWewCHA3fk6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\nWRH/H+lQIDQk/4yrAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"zQUyF4TrfP9Y","colab_type":"code","colab":{}},"source":["desvio_padrao = tips[\"tempo_permanencia\"].std()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"PYffVtcUiqwg","colab_type":"code","outputId":"361c5645-51e9-4f7c-c8b0-52956308c50c","executionInfo":{"status":"ok","timestamp":1566346944613,"user_tz":180,"elapsed":651,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["#valor mínimo do intervalo de 95%\n","minimo_95 = media - 2*desvio_padrao\n","\n","#valor máximo do intervalo de 95%\n","maximo_95 = media + 2*desvio_padrao\n","\n","print(\"Intervalo tempo permanência que representa 95% dos dados:\", round(minimo_95, 2), \"min -\", round(maximo_95, 2), \"min\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Intervalo tempo permanência que representa 95% dos dados: 29.95 min - 50.58 min\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ISkUm5BUzro7","colab_type":"text"},"source":["**Quais os intervalos de tempo estão os 68% mais frequentes?**"]},{"cell_type":"code","metadata":{"id":"nensd8etxNdg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"950c0dab-0a0c-4d00-e442-ced23072d012","executionInfo":{"status":"ok","timestamp":1566434509891,"user_tz":180,"elapsed":615,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}}},"source":["#valor mínimo do intervalo de 68.2%\n","minimo_68 = media - desvio_padrao\n","\n","#valor mínimo do intervalo de 68.2%\n","maximo_68 = media + desvio_padrao\n","print(minimo_68, maximo_68)"],"execution_count":13,"outputs":[{"output_type":"stream","text":["35.10501044425533 45.41957971967909\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"pMeyCQSxxfJQ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":90},"outputId":"e083d894-a59a-4d73-c237-c50a2db0bd44","executionInfo":{"status":"ok","timestamp":1566434733569,"user_tz":180,"elapsed":647,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}}},"source":["condicao=[tips['tempo_permanencia']<=35.10,tips['tempo_permanencia']<=45.41, tips['tempo_permanencia']>45.41]\n","rotulo=['permanencia <= 35.10 min','permanencia <= 45.41 min', 'permanencia > 45.41 min']\n","\n","tips['tempo_categ']=np.select(condicao,rotulo)\n","tips['tempo_categ'].value_counts()"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["permanencia <= 45.41 min 155\n","permanencia > 45.41 min 47\n","permanencia <= 35.10 min 42\n","Name: tempo_categ, dtype: int64"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"yCHkoBDfzbP_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"f795114c-2fb9-407e-83c0-ff38b69d5b3b","executionInfo":{"status":"ok","timestamp":1566435085653,"user_tz":180,"elapsed":601,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}}},"source":["#utilizando o intervalo de tempo de permanência <= 45.41 min\n","155/244*100"],"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["63.52459016393443"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"markdown","metadata":{"id":"XsSTfHZl2H9F","colab_type":"text"},"source":["**Quais os intervalos de tempo estão os 84% mais frequentes?**"]},{"cell_type":"code","metadata":{"id":"fT0YUxja1fMK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":53},"outputId":"258c7a7b-1cc3-4f17-92c4-c99be483f4fd","executionInfo":{"status":"ok","timestamp":1566436997335,"user_tz":180,"elapsed":654,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}}},"source":["print(desvio_padrao, media)\n","\n","#Z = (b - media)/desvio padrão\n","#Z foi obtido na tabela de distribuição normal\n","#na tabela de distribuição normal: 0.50 + metade 0.84 (0.41) = 0.91\n","#Z=1.41 e -1.41\n","#intervalo na normal padrão que tem 84% é [-1.41,1.41]\n","#e voltando Z = (b - media)/desvio padrão\n","#1.41 = (b - media)/desvio padrão\n","\n","(1.41*desvio_padrao) + media\n"],"execution_count":20,"outputs":[{"output_type":"stream","text":["5.157284637711886 40.26229508196721\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["47.53406642114097"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"markdown","metadata":{"id":"ndiNhf5U3fzB","colab_type":"text"},"source":["**Considerando que valores de tempo permanência estão ordenados, quais são os 84% primeiros dos dados?**"]},{"cell_type":"code","metadata":{"id":"yA_i7_fy7gZD","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":35},"outputId":"b0cb0879-9f50-435d-8596-3cf389f78be9","executionInfo":{"status":"ok","timestamp":1566437249018,"user_tz":180,"elapsed":525,"user":{"displayName":"Mariana Dias Guilardi","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAut-cUcJykVJMwwrbAjQ7-Ypz5Grj18EnI0RH4Lg=s64","userId":"01637865623260601838"}}},"source":["#Z = (b - media)/desvio padrão\n","#Z foi obtido na tabela de distribuição normal\n","#na tabela de distribuição normal: 0.84: Z=1\n","#e voltando Z = (b - media)/desvio padrão\n","#1.41 = (b - media)/desvio padrão\n","1*desvio_padrao + media"],"execution_count":22,"outputs":[{"output_type":"execute_result","data":{"text/plain":["45.41957971967909"]},"metadata":{"tags":[]},"execution_count":22}]}]} -------------------------------------------------------------------------------- /Trilha_de_Estudos/07 material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/07 material.pdf -------------------------------------------------------------------------------- /Trilha_de_Estudos/08 material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/08 material.pdf -------------------------------------------------------------------------------- /Trilha_de_Estudos/08 notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "#
Teste de Hipótese
#" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Nossa hipótese é que em média uma pessoa gasta 90 minutos no trajeto Diadema - Faria Lima em cada uma de suas viagens. Testaremos esta percepção ('achismo') através de um teste de hipótese." 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import numpy as np\n", 24 | "import pandas as pd\n", 25 | "import matplotlib.pyplot as plt" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "metadata": {}, 31 | "source": [ 32 | "### Documentação np.random.normal : \n", 33 | "https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.normal.html

\n", 34 | "media = 1 parametro
\n", 35 | "scale = desvio padrão
\n", 36 | "size = tamanho da amostra
" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 8, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "#criando uma amostra usando np.random.normal (ida) \n", 46 | "np.random.seed(seed = 10)\n", 47 | "tempo_transporte = np.random.normal(87, 10, size = 90)" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 3, 53 | "metadata": {}, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "image/png": "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\n", 58 | "text/plain": [ 59 | "
" 60 | ] 61 | }, 62 | "metadata": { 63 | "needs_background": "light" 64 | }, 65 | "output_type": "display_data" 66 | } 67 | ], 68 | "source": [ 69 | "#plot histograma (ida)\n", 70 | "plt.hist(tempo_transporte, bins = 40);" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/plain": [ 81 | "87.81055665867586" 82 | ] 83 | }, 84 | "execution_count": 4, 85 | "metadata": {}, 86 | "output_type": "execute_result" 87 | } 88 | ], 89 | "source": [ 90 | "#análise média (ida)\n", 91 | "np.mean(tempo_transporte)" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 5, 97 | "metadata": {}, 98 | "outputs": [ 99 | { 100 | "data": { 101 | "text/plain": [ 102 | "87.51720323384639" 103 | ] 104 | }, 105 | "execution_count": 5, 106 | "metadata": {}, 107 | "output_type": "execute_result" 108 | } 109 | ], 110 | "source": [ 111 | "#análise mediana\n", 112 | "np.median(tempo_transporte)" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 6, 118 | "metadata": {}, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "9.752625790712477" 124 | ] 125 | }, 126 | "execution_count": 6, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | } 130 | ], 131 | "source": [ 132 | "#análise devio padrão\n", 133 | "np.std(tempo_transporte)" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 20, 139 | "metadata": {}, 140 | "outputs": [], 141 | "source": [ 142 | "#criando uma amostra usando np.random.normal (ida) \n", 143 | "np.random.seed(seed = 10)\n", 144 | "tempo_transporte2 = np.random.normal(87, 10, size = 9000)" 145 | ] 146 | }, 147 | { 148 | "cell_type": "code", 149 | "execution_count": 21, 150 | "metadata": {}, 151 | "outputs": [ 152 | { 153 | "data": { 154 | "image/png": "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\n", 155 | "text/plain": [ 156 | "
" 157 | ] 158 | }, 159 | "metadata": { 160 | "needs_background": "light" 161 | }, 162 | "output_type": "display_data" 163 | } 164 | ], 165 | "source": [ 166 | "plt.hist(tempo_transporte2, bins = 40);" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 22, 172 | "metadata": {}, 173 | "outputs": [ 174 | { 175 | "data": { 176 | "text/plain": [ 177 | "(87.00330097985307, 9.906340700090917)" 178 | ] 179 | }, 180 | "execution_count": 22, 181 | "metadata": {}, 182 | "output_type": "execute_result" 183 | } 184 | ], 185 | "source": [ 186 | "mean2 = np.mean(tempo_transporte2)\n", 187 | "std2 = np.std(tempo_transporte2)\n", 188 | "(mean2, std2)" 189 | ] 190 | }, 191 | { 192 | "attachments": { 193 | "image.png": { 194 | "image/png": "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" 195 | } 196 | }, 197 | "cell_type": "markdown", 198 | "metadata": {}, 199 | "source": [ 200 | "### Passos:\n", 201 | "**1)** Qual o nível de significância que queremos assumir? *Qual a confiança de nosso teste?*
\n", 202 | "**2)** Será que a nossa média está dentro do nosso intervalo ideal, 90min?

\n", 203 | "*lembrando que eu só rejeito o que está muito distante, uma vez que falamos que é em média 90min*\n", 204 | "\n", 205 | "notamos que nossa média amostral é de aprox. 87.81\n", 206 | "\n", 207 | "tabela: https://www.topinvest.com.br/distribuicao-normal/\n", 208 | "\n", 209 | "fórmula Z-Score (normalização pros manos)\n", 210 | "\n", 211 | "![image.png](attachment:image.png) -=> vamos pesquisar\n", 212 | "Depende do tamanho da amostra a gente utiliza ou a fórmula acima ou essa:\n", 213 | "\n", 214 | "z = (x-mean_sample)/std" 215 | ] 216 | }, 217 | { 218 | "cell_type": "markdown", 219 | "metadata": {}, 220 | "source": [ 221 | "### Desafio:\n", 222 | "\n", 223 | "Com esse valor acima de média e assumindo 5% de nível de significância, podemos falar que nâo temos evidências para rejeitar a nossa hipótese (tempo médio da viagem Diadema - Faria Lima = 90)?\n", 224 | "\n", 225 | "\n" 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": 12, 231 | "metadata": {}, 232 | "outputs": [], 233 | "source": [ 234 | "#valores\n", 235 | "x = 90\n", 236 | "mean_sample = 87.81\n", 237 | "std = np.std(tempo_transporte)\n", 238 | "n = np.sqrt(90)" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 13, 244 | "metadata": {}, 245 | "outputs": [ 246 | { 247 | "name": "stdout", 248 | "output_type": "stream", 249 | "text": [ 250 | "O valor de z é: 0.2245549093133007\n" 251 | ] 252 | } 253 | ], 254 | "source": [ 255 | "#localizando na tabela z os valores\n", 256 | "z = (x-mean_sample) / std\n", 257 | "print('O valor de z é:', z)" 258 | ] 259 | }, 260 | { 261 | "attachments": {}, 262 | "cell_type": "markdown", 263 | "metadata": {}, 264 | "source": [ 265 | "Traduzimos a informação da nossa amostra para a normal padrão (escala 0 - 1).\n", 266 | "Nossa significância é de 5%, ou seja, aceitamos errar 5% ou acertar 95%. Para descobrirmos se nosso valor está dentro desta margem buscamos na tabela Z onde é que está 95%. \n", 267 | "\n", 268 | "O valor de 95% de confiança está em 1.96. Como a normal tem dois lados a área sobre a nossa curva, observamos que 0.22 está dentro da área de não rejeitarmos a nossa hipótese nula." 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "Para sabermos se a gente rejeita ou não nossa hipótese precisamos checar se o nosso nível de significância ...... através doa fóruma de z.alfa/2 " 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 37, 281 | "metadata": {}, 282 | "outputs": [], 283 | "source": [ 284 | "tempo_transporte3 = np.random.normal(20, 4.75, size = 9000)" 285 | ] 286 | }, 287 | { 288 | "cell_type": "code", 289 | "execution_count": 38, 290 | "metadata": {}, 291 | "outputs": [ 292 | { 293 | "data": { 294 | "image/png": "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\n", 295 | "text/plain": [ 296 | "
" 297 | ] 298 | }, 299 | "metadata": { 300 | "needs_background": "light" 301 | }, 302 | "output_type": "display_data" 303 | } 304 | ], 305 | "source": [ 306 | "plt.hist(tempo_transporte3, bins = 40);" 307 | ] 308 | }, 309 | { 310 | "cell_type": "code", 311 | "execution_count": 33, 312 | "metadata": {}, 313 | "outputs": [], 314 | "source": [ 315 | "x3 = 90\n", 316 | "mean_sample3 = 20\n", 317 | "std3 = np.std(tempo_transporte3)\n", 318 | "n3 = np.sqrt(90)" 319 | ] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "execution_count": 36, 324 | "metadata": {}, 325 | "outputs": [ 326 | { 327 | "data": { 328 | "text/plain": [ 329 | "14.700947445868836" 330 | ] 331 | }, 332 | "execution_count": 36, 333 | "metadata": {}, 334 | "output_type": "execute_result" 335 | } 336 | ], 337 | "source": [ 338 | "z3 = (x3-mean_sample3)/std3\n", 339 | "z3" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "para próxima aula a gente verá o TIPS para enxergamos se mulheres dão + tips do que os homens =) - pegar a base e colocar no notebook" 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": null, 352 | "metadata": {}, 353 | "outputs": [], 354 | "source": [] 355 | } 356 | ], 357 | "metadata": { 358 | "kernelspec": { 359 | "display_name": "Python 3", 360 | "language": "python", 361 | "name": "python3" 362 | }, 363 | "language_info": { 364 | "codemirror_mode": { 365 | "name": "ipython", 366 | "version": 3 367 | }, 368 | "file_extension": ".py", 369 | "mimetype": "text/x-python", 370 | "name": "python", 371 | "nbconvert_exporter": "python", 372 | "pygments_lexer": "ipython3", 373 | "version": "3.7.3" 374 | } 375 | }, 376 | "nbformat": 4, 377 | "nbformat_minor": 2 378 | } 379 | -------------------------------------------------------------------------------- /Trilha_de_Estudos/09 material.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/09 material.pdf -------------------------------------------------------------------------------- /Trilha_de_Estudos/09 notebook.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "kernelspec": { 6 | "display_name": "Python 3", 7 | "language": "python", 8 | "name": "python3" 9 | }, 10 | "language_info": { 11 | "codemirror_mode": { 12 | "name": "ipython", 13 | "version": 3 14 | }, 15 | "file_extension": ".py", 16 | "mimetype": "text/x-python", 17 | "name": "python", 18 | "nbconvert_exporter": "python", 19 | "pygments_lexer": "ipython3", 20 | "version": "3.7.3" 21 | }, 22 | "colab": { 23 | "name": "Teste de Hipótese (3).ipynb", 24 | "provenance": [], 25 | "collapsed_sections": [] 26 | } 27 | }, 28 | "cells": [ 29 | { 30 | "cell_type": "markdown", 31 | "metadata": { 32 | "id": "xlSZwibooixM", 33 | "colab_type": "text" 34 | }, 35 | "source": [ 36 | "#
Teste de Hipótese
#" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": { 42 | "id": "HuF48BnUoixP", 43 | "colab_type": "text" 44 | }, 45 | "source": [ 46 | "Nossa hipótese é que em média uma pessoa gasta 90 minutos no trajeto Diadema - Faria Lima em cada uma de suas viagens. Testaremos esta percepção ('achismo') através de um teste de hipótese." 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "metadata": { 52 | "id": "wY7N7NOLoixT", 53 | "colab_type": "code", 54 | "colab": {} 55 | }, 56 | "source": [ 57 | "import numpy as np\n", 58 | "import pandas as pd\n", 59 | "import matplotlib.pyplot as plt" 60 | ], 61 | "execution_count": null, 62 | "outputs": [] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": { 67 | "id": "AzONNVnkoixk", 68 | "colab_type": "text" 69 | }, 70 | "source": [ 71 | "### Documentação np.random.normal : \n", 72 | "https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.random.normal.html

\n", 73 | "media = 1 parametro
\n", 74 | "scale = desvio padrão
\n", 75 | "size = tamanho da amostra
" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "metadata": { 81 | "id": "M_pWAO7toixo", 82 | "colab_type": "code", 83 | "colab": {} 84 | }, 85 | "source": [ 86 | "#criando uma amostra usando np.random.normal (ida) \n", 87 | "np.random.seed(seed = 10)\n", 88 | "tempo_transporte = np.random.normal(87, 10, size = 90)" 89 | ], 90 | "execution_count": null, 91 | "outputs": [] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "metadata": { 96 | "id": "3gGWgzhnoix0", 97 | "colab_type": "code", 98 | "colab": {}, 99 | "outputId": "74251240-e1c8-4843-eb50-dfc51189b570" 100 | }, 101 | "source": [ 102 | "#plot histograma (ida)\n", 103 | "plt.hist(tempo_transporte, bins = 40);" 104 | ], 105 | "execution_count": null, 106 | "outputs": [ 107 | { 108 | "output_type": "display_data", 109 | "data": { 110 | "image/png": "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\n", 111 | "text/plain": [ 112 | "
" 113 | ] 114 | }, 115 | "metadata": { 116 | "tags": [], 117 | "needs_background": "light" 118 | } 119 | } 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "metadata": { 125 | "id": "b2xwTy7loiyD", 126 | "colab_type": "code", 127 | "colab": {}, 128 | "outputId": "c8ca5857-f3b7-4630-d8aa-ff3bad39a3a2" 129 | }, 130 | "source": [ 131 | "#análise média (ida)\n", 132 | "np.mean(tempo_transporte)" 133 | ], 134 | "execution_count": null, 135 | "outputs": [ 136 | { 137 | "output_type": "execute_result", 138 | "data": { 139 | "text/plain": [ 140 | "87.81055665867586" 141 | ] 142 | }, 143 | "metadata": { 144 | "tags": [] 145 | }, 146 | "execution_count": 4 147 | } 148 | ] 149 | }, 150 | { 151 | "cell_type": "code", 152 | "metadata": { 153 | "id": "kPkYU1eUoiyR", 154 | "colab_type": "code", 155 | "colab": {}, 156 | "outputId": "c5300c27-0bef-4b5d-df27-112be8c43bb5" 157 | }, 158 | "source": [ 159 | "#análise mediana\n", 160 | "np.median(tempo_transporte)" 161 | ], 162 | "execution_count": null, 163 | "outputs": [ 164 | { 165 | "output_type": "execute_result", 166 | "data": { 167 | "text/plain": [ 168 | "87.51720323384639" 169 | ] 170 | }, 171 | "metadata": { 172 | "tags": [] 173 | }, 174 | "execution_count": 5 175 | } 176 | ] 177 | }, 178 | { 179 | "cell_type": "code", 180 | "metadata": { 181 | "id": "5VOhALjooiyd", 182 | "colab_type": "code", 183 | "colab": {}, 184 | "outputId": "08799949-c7c2-42b5-b7c1-9e1033047fb8" 185 | }, 186 | "source": [ 187 | "#análise devio padrão\n", 188 | "np.std(tempo_transporte)" 189 | ], 190 | "execution_count": null, 191 | "outputs": [ 192 | { 193 | "output_type": "execute_result", 194 | "data": { 195 | "text/plain": [ 196 | "9.752625790712477" 197 | ] 198 | }, 199 | "metadata": { 200 | "tags": [] 201 | }, 202 | "execution_count": 6 203 | } 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "metadata": { 209 | "id": "jKyE0O54oiyo", 210 | "colab_type": "code", 211 | "colab": {} 212 | }, 213 | "source": [ 214 | "#criando uma amostra usando np.random.normal (ida) \n", 215 | "np.random.seed(seed = 10)\n", 216 | "tempo_transporte2 = np.random.normal(87, 10, size = 9000)" 217 | ], 218 | "execution_count": null, 219 | "outputs": [] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "metadata": { 224 | "id": "oTTxA9b2oiyv", 225 | "colab_type": "code", 226 | "colab": {}, 227 | "outputId": "2785adfa-feb9-45d7-dca0-b0bf4e4becd7" 228 | }, 229 | "source": [ 230 | "plt.hist(tempo_transporte2, bins = 40);" 231 | ], 232 | "execution_count": null, 233 | "outputs": [ 234 | { 235 | "output_type": "display_data", 236 | "data": { 237 | "image/png": "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\n", 238 | "text/plain": [ 239 | "
" 240 | ] 241 | }, 242 | "metadata": { 243 | "tags": [], 244 | "needs_background": "light" 245 | } 246 | } 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "metadata": { 252 | "id": "cbD5JkPjoiy4", 253 | "colab_type": "code", 254 | "colab": {}, 255 | "outputId": "cefe481f-9477-4b4b-afac-d19993c585e4" 256 | }, 257 | "source": [ 258 | "mean2 = np.mean(tempo_transporte2)\n", 259 | "std2 = np.std(tempo_transporte2)\n", 260 | "(mean2, std2)" 261 | ], 262 | "execution_count": null, 263 | "outputs": [ 264 | { 265 | "output_type": "execute_result", 266 | "data": { 267 | "text/plain": [ 268 | "(87.00330097985307, 9.906340700090917)" 269 | ] 270 | }, 271 | "metadata": { 272 | "tags": [] 273 | }, 274 | "execution_count": 22 275 | } 276 | ] 277 | }, 278 | { 279 | "cell_type": "markdown", 280 | "metadata": { 281 | "id": "nfJrE6wooizA", 282 | "colab_type": "text" 283 | }, 284 | "source": [ 285 | "### Passos:\n", 286 | "**1)** Qual o nível de significância que queremos assumir? *Qual a confiança de nosso teste?*
\n", 287 | "**2)** Será que a nossa média está dentro do nosso intervalo ideal, 90min?

\n", 288 | "*lembrando que eu só rejeito o que está muito distante, uma vez que falamos que é em média 90min*\n", 289 | "\n", 290 | "notamos que nossa média amostral é de aprox. 87.81\n", 291 | "\n", 292 | "tabela: https://www.topinvest.com.br/distribuicao-normal/\n", 293 | "\n", 294 | "fórmula Z-Score (normalização pros manos)\n", 295 | "\n", 296 | "![image.png](attachment:image.png) -=> vamos pesquisar\n", 297 | "Depende do tamanho da amostra a gente utiliza ou a fórmula acima ou essa:\n", 298 | "\n", 299 | "z = (x-mean_sample)/std" 300 | ] 301 | }, 302 | { 303 | "cell_type": "markdown", 304 | "metadata": { 305 | "id": "7Po6iAPzoizC", 306 | "colab_type": "text" 307 | }, 308 | "source": [ 309 | "### Desafio:\n", 310 | "\n", 311 | "Com esse valor acima de média e assumindo 5% de nível de significância, podemos falar que nâo temos evidências para rejeitar a nossa hipótese (tempo médio da viagem Diadema - Faria Lima = 90)?\n", 312 | "\n", 313 | "\n" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "metadata": { 319 | "id": "rj_4r-JAoizE", 320 | "colab_type": "code", 321 | "colab": {} 322 | }, 323 | "source": [ 324 | "#valores\n", 325 | "x = 90\n", 326 | "mean_sample = 87.81\n", 327 | "std = np.std(tempo_transporte)\n", 328 | "n = np.sqrt(90)" 329 | ], 330 | "execution_count": null, 331 | "outputs": [] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "metadata": { 336 | "id": "0IDx7rxGoiza", 337 | "colab_type": "code", 338 | "colab": {}, 339 | "outputId": "b8920eec-7a20-47a0-b297-fe67387d7b96" 340 | }, 341 | "source": [ 342 | "#localizando na tabela z os valores\n", 343 | "z = (x-mean_sample) / std\n", 344 | "print('O valor de z é:', z)" 345 | ], 346 | "execution_count": null, 347 | "outputs": [ 348 | { 349 | "output_type": "stream", 350 | "text": [ 351 | "O valor de z é: 0.2245549093133007\n" 352 | ], 353 | "name": "stdout" 354 | } 355 | ] 356 | }, 357 | { 358 | "cell_type": "markdown", 359 | "metadata": { 360 | "id": "OZBdu_Hxoizj", 361 | "colab_type": "text" 362 | }, 363 | "source": [ 364 | "Traduzimos a informação da nossa amostra para a normal padrão (escala 0 - 1).\n", 365 | "Nossa significância é de 5%, ou seja, aceitamos errar 5% ou acertar 95%. Para descobrirmos se nosso valor está dentro desta margem buscamos na tabela Z onde é que está 95%. \n", 366 | "\n", 367 | "O valor de 95% de confiança está em 1.96. Como a normal tem dois lados a área sobre a nossa curva, observamos que 0.22 está dentro da área de não rejeitarmos a nossa hipótese nula." 368 | ] 369 | }, 370 | { 371 | "cell_type": "markdown", 372 | "metadata": { 373 | "id": "_LTxs3wyoizk", 374 | "colab_type": "text" 375 | }, 376 | "source": [ 377 | "Para sabermos se a gente rejeita ou não nossa hipótese precisamos checar se o nosso nível de significância ...... através doa fóruma de z.alfa/2 " 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "metadata": { 383 | "id": "aPl6KYsAoizm", 384 | "colab_type": "code", 385 | "colab": {} 386 | }, 387 | "source": [ 388 | "tempo_transporte3 = np.random.normal(20, 4.75, size = 9000)" 389 | ], 390 | "execution_count": null, 391 | "outputs": [] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "metadata": { 396 | "id": "hRXqh4z6oizw", 397 | "colab_type": "code", 398 | "colab": {}, 399 | "outputId": "8d8df9ac-92d5-490a-e74d-3a5d0678abad" 400 | }, 401 | "source": [ 402 | "plt.hist(tempo_transporte3, bins = 40);" 403 | ], 404 | "execution_count": null, 405 | "outputs": [ 406 | { 407 | "output_type": "display_data", 408 | "data": { 409 | "image/png": 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\n", 410 | "text/plain": [ 411 | "
" 412 | ] 413 | }, 414 | "metadata": { 415 | "tags": [], 416 | "needs_background": "light" 417 | } 418 | } 419 | ] 420 | }, 421 | { 422 | "cell_type": "code", 423 | "metadata": { 424 | "id": "9e4BaieCoiz3", 425 | "colab_type": "code", 426 | "colab": {} 427 | }, 428 | "source": [ 429 | "x3 = 90\n", 430 | "mean_sample3 = 20\n", 431 | "std3 = np.std(tempo_transporte3)\n", 432 | "n3 = np.sqrt(90)" 433 | ], 434 | "execution_count": null, 435 | "outputs": [] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "metadata": { 440 | "id": "0_Cfyfa3oi0B", 441 | "colab_type": "code", 442 | "colab": {}, 443 | "outputId": "0911c68a-8783-40bf-be41-308633585053" 444 | }, 445 | "source": [ 446 | "z3 = (x3-mean_sample3)/std3\n", 447 | "z3" 448 | ], 449 | "execution_count": null, 450 | "outputs": [ 451 | { 452 | "output_type": "execute_result", 453 | "data": { 454 | "text/plain": [ 455 | "14.700947445868836" 456 | ] 457 | }, 458 | "metadata": { 459 | "tags": [] 460 | }, 461 | "execution_count": 36 462 | } 463 | ] 464 | } 465 | ] 466 | } -------------------------------------------------------------------------------- /Trilha_de_Estudos/Cronograma Estudos - GEDS.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/Cronograma Estudos - GEDS.xlsx -------------------------------------------------------------------------------- /Trilha_de_Estudos/Desafio Completo_ Valor de Venda Imóveis.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/Desafio Completo_ Valor de Venda Imóveis.pdf -------------------------------------------------------------------------------- /Trilha_de_Estudos/README.md: -------------------------------------------------------------------------------- 1 | ### O conteúdo na trilha de estudos está apresentado da seguinte forma: 2 | 3 | 01 material. Probabilidade básica - Introdução + Exercícios 4 | 5 | 02 material. Probabilidade básica - Propriedades 6 | 7 | 03 material. Probabilidade básica - Condicional 8 | 9 | 04 material. Probabilidade básica - Teorema de Bayes 10 | 11 | 05 material. Introdução a Variáveis Aleatórias 12 | 13 | 06 material. Introdução a Variáveis Aleatórias Contínuas - Distribuição Normal 14 | 15 | 07 material. Introdução à Amostragem e Inferência Estatística 16 | 17 | 08 material. Teste de Hipótese I 18 | 19 | 09 material. Teste de Hipótese II 20 | -------------------------------------------------------------------------------- /Trilha_de_Estudos/folhaprodam2018_anonima.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/folhaprodam2018_anonima.csv -------------------------------------------------------------------------------- /Trilha_de_Estudos/proposta_estudo.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/Trilha_de_Estudos/proposta_estudo.pdf -------------------------------------------------------------------------------- /Trilha_de_Estudos/respostas_material_02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Semana 2\n", 8 | "## *Probabilidade básica - Propriedades*\n" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "metadata": {}, 15 | "outputs": [], 16 | "source": [ 17 | "#importanto as bibliotecas\n", 18 | "import pandas as pd\n", 19 | "import numpy as np" 20 | ] 21 | }, 22 | { 23 | "cell_type": "markdown", 24 | "metadata": {}, 25 | "source": [ 26 | "### 1. Foram coletados os dados de 16 meninas, sobre Escolaridade e se Trabalham ou não como programadoras." 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 2, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/html": [ 37 | "
\n", 38 | "\n", 51 | "\n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | "
escolaridadenãosim
0superior completo16
1superior incompleto33
2ensino médio21
\n", 81 | "
" 82 | ], 83 | "text/plain": [ 84 | " escolaridade não sim\n", 85 | "0 superior completo 1 6\n", 86 | "1 superior incompleto 3 3\n", 87 | "2 ensino médio 2 1" 88 | ] 89 | }, 90 | "execution_count": 2, 91 | "metadata": {}, 92 | "output_type": "execute_result" 93 | } 94 | ], 95 | "source": [ 96 | "dic1 = {'escolaridade' : ['superior completo', 'superior incompleto', 'ensino médio'],\n", 97 | " 'não': [1,3,2], 'sim' : [6,3,1]}\n", 98 | "pyladies_escolaridade = pd.DataFrame(dic1)\n", 99 | "pyladies_escolaridade.head()" 100 | ] 101 | }, 102 | { 103 | "cell_type": "markdown", 104 | "metadata": {}, 105 | "source": [ 106 | "1a. Qual a probabilidade dela ter Superior Completo **E (interseção)** Trabalhar como programadora?" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 3, 112 | "metadata": {}, 113 | "outputs": [ 114 | { 115 | "data": { 116 | "text/plain": [ 117 | "0.4375" 118 | ] 119 | }, 120 | "execution_count": 3, 121 | "metadata": {}, 122 | "output_type": "execute_result" 123 | } 124 | ], 125 | "source": [ 126 | "# probalidade do ensino completo (P(A))\n", 127 | "7/16" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 4, 133 | "metadata": {}, 134 | "outputs": [ 135 | { 136 | "data": { 137 | "text/plain": [ 138 | "0.625" 139 | ] 140 | }, 141 | "execution_count": 4, 142 | "metadata": {}, 143 | "output_type": "execute_result" 144 | } 145 | ], 146 | "source": [ 147 | "#probalidade de tralhar como programadora (P(B))\n", 148 | "10/16" 149 | ] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "metadata": {}, 154 | "source": [ 155 | "$ P(A) \\cap P(B)$" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 5, 161 | "metadata": {}, 162 | "outputs": [ 163 | { 164 | "data": { 165 | "text/plain": [ 166 | "0.375" 167 | ] 168 | }, 169 | "execution_count": 5, 170 | "metadata": {}, 171 | "output_type": "execute_result" 172 | } 173 | ], 174 | "source": [ 175 | "#interseção\n", 176 | "6/16" 177 | ] 178 | }, 179 | { 180 | "cell_type": "markdown", 181 | "metadata": {}, 182 | "source": [ 183 | "1b. probabilidade dela ter **até** o Ensino Médio?" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 6, 189 | "metadata": {}, 190 | "outputs": [ 191 | { 192 | "data": { 193 | "text/html": [ 194 | "
\n", 195 | "\n", 208 | "\n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | "
escolaridadenãosimtotal
0superior completo167
1superior incompleto336
2ensino médio213
\n", 242 | "
" 243 | ], 244 | "text/plain": [ 245 | " escolaridade não sim total\n", 246 | "0 superior completo 1 6 7\n", 247 | "1 superior incompleto 3 3 6\n", 248 | "2 ensino médio 2 1 3" 249 | ] 250 | }, 251 | "execution_count": 6, 252 | "metadata": {}, 253 | "output_type": "execute_result" 254 | } 255 | ], 256 | "source": [ 257 | "pyladies_escolaridade['total'] = pyladies_escolaridade.sim + pyladies_escolaridade.não\n", 258 | "pyladies_escolaridade.head()" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": 7, 264 | "metadata": {}, 265 | "outputs": [ 266 | { 267 | "data": { 268 | "text/plain": [ 269 | "16" 270 | ] 271 | }, 272 | "execution_count": 7, 273 | "metadata": {}, 274 | "output_type": "execute_result" 275 | } 276 | ], 277 | "source": [ 278 | "sum(pyladies_escolaridade.total)" 279 | ] 280 | }, 281 | { 282 | "cell_type": "code", 283 | "execution_count": 8, 284 | "metadata": {}, 285 | "outputs": [ 286 | { 287 | "data": { 288 | "text/plain": [ 289 | "0.1875" 290 | ] 291 | }, 292 | "execution_count": 8, 293 | "metadata": {}, 294 | "output_type": "execute_result" 295 | } 296 | ], 297 | "source": [ 298 | "#ter apenas o ensino médio\n", 299 | "3/16" 300 | ] 301 | }, 302 | { 303 | "cell_type": "markdown", 304 | "metadata": {}, 305 | "source": [ 306 | "1c. Qual a probabilidade dela **ter além** do Ensino Médio?" 307 | ] 308 | }, 309 | { 310 | "cell_type": "markdown", 311 | "metadata": {}, 312 | "source": [ 313 | "Propiedade da probabilidade\n", 314 | "\n", 315 | "$ P(A) + P(B) = 1 $\n", 316 | "\n", 317 | "P(ter colegial) = $3/16$\n", 318 | "\n", 319 | "P(ter as duas coisas) = 16/16 = 1\n" 320 | ] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "execution_count": 9, 325 | "metadata": {}, 326 | "outputs": [ 327 | { 328 | "data": { 329 | "text/plain": [ 330 | "0.8125" 331 | ] 332 | }, 333 | "execution_count": 9, 334 | "metadata": {}, 335 | "output_type": "execute_result" 336 | } 337 | ], 338 | "source": [ 339 | "(1-3/16) #13/16" 340 | ] 341 | }, 342 | { 343 | "cell_type": "markdown", 344 | "metadata": {}, 345 | "source": [ 346 | "1d. Qual a probabilidade dela ter **apenas** Ensino Médio **ou** não trabalhar como programadora?" 347 | ] 348 | }, 349 | { 350 | "cell_type": "code", 351 | "execution_count": 10, 352 | "metadata": {}, 353 | "outputs": [ 354 | { 355 | "data": { 356 | "text/plain": [ 357 | "0.1875" 358 | ] 359 | }, 360 | "execution_count": 10, 361 | "metadata": {}, 362 | "output_type": "execute_result" 363 | } 364 | ], 365 | "source": [ 366 | "#ensino médio\n", 367 | "3/16" 368 | ] 369 | }, 370 | { 371 | "cell_type": "code", 372 | "execution_count": 11, 373 | "metadata": {}, 374 | "outputs": [ 375 | { 376 | "data": { 377 | "text/plain": [ 378 | "0.375" 379 | ] 380 | }, 381 | "execution_count": 11, 382 | "metadata": {}, 383 | "output_type": "execute_result" 384 | } 385 | ], 386 | "source": [ 387 | "sum(pyladies_escolaridade.não)/16 #6/16" 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "metadata": {}, 393 | "source": [ 394 | "$ P(A) + P(B) - (P(A) \\cap P(B))$" 395 | ] 396 | }, 397 | { 398 | "cell_type": "code", 399 | "execution_count": 12, 400 | "metadata": {}, 401 | "outputs": [ 402 | { 403 | "data": { 404 | "text/plain": [ 405 | "0.4375" 406 | ] 407 | }, 408 | "execution_count": 12, 409 | "metadata": {}, 410 | "output_type": "execute_result" 411 | } 412 | ], 413 | "source": [ 414 | "3/16+6/16-2/16 # = 7/16" 415 | ] 416 | }, 417 | { 418 | "cell_type": "markdown", 419 | "metadata": {}, 420 | "source": [ 421 | "### 2. Com o data set tips vamos pensar nas seguintes situações:\n", 422 | "a. Escolhido aleatoriamente uma observação do data set, qual a probabilidade de termos uma mulher fumante?\n", 423 | "\n", 424 | "b. E quando é mais provável ter clientes fumantes (independente do sexo), durante a semana ou aos finais de semana?" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": 13, 430 | "metadata": {}, 431 | "outputs": [], 432 | "source": [ 433 | "#importando o dataset tips\n", 434 | "tips = pd.read_csv('https://raw.githubusercontent.com/PyLadiesSP/data-science/master/workshops/workshop_introdu%C3%A7%C3%A3o_estatistica_pandas/tips.csv')" 435 | ] 436 | }, 437 | { 438 | "cell_type": "code", 439 | "execution_count": 14, 440 | "metadata": {}, 441 | "outputs": [ 442 | { 443 | "data": { 444 | "text/html": [ 445 | "
\n", 446 | "\n", 459 | "\n", 460 | " \n", 461 | " \n", 462 | " \n", 463 | " \n", 464 | " \n", 465 | " \n", 466 | " \n", 467 | " \n", 468 | " \n", 469 | " \n", 470 | " \n", 471 | " \n", 472 | " \n", 473 | " \n", 474 | " \n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | " \n", 515 | " \n", 516 | " \n", 517 | " \n", 518 | " \n", 519 | " \n", 520 | " \n", 521 | " \n", 522 | " \n", 523 | " \n", 524 | " \n", 525 | " \n", 526 | " \n", 527 | " \n", 528 | " \n", 529 | " \n", 530 | "
total_contagorjetagenerofumantediahorariopessoas_mesatempo_permanencia
016.991.01Femininonaodomjantar241
110.341.66Masculinonaodomjantar340
221.013.50Masculinonaodomjantar349
323.683.31Masculinonaodomjantar243
424.593.61Femininonaodomjantar434
\n", 531 | "
" 532 | ], 533 | "text/plain": [ 534 | " total_conta gorjeta genero fumante dia horario pessoas_mesa \\\n", 535 | "0 16.99 1.01 Feminino nao dom jantar 2 \n", 536 | "1 10.34 1.66 Masculino nao dom jantar 3 \n", 537 | "2 21.01 3.50 Masculino nao dom jantar 3 \n", 538 | "3 23.68 3.31 Masculino nao dom jantar 2 \n", 539 | "4 24.59 3.61 Feminino nao dom jantar 4 \n", 540 | "\n", 541 | " tempo_permanencia \n", 542 | "0 41 \n", 543 | "1 40 \n", 544 | "2 49 \n", 545 | "3 43 \n", 546 | "4 34 " 547 | ] 548 | }, 549 | "execution_count": 14, 550 | "metadata": {}, 551 | "output_type": "execute_result" 552 | } 553 | ], 554 | "source": [ 555 | "#confirmando a importação\n", 556 | "tips.head()" 557 | ] 558 | }, 559 | { 560 | "cell_type": "markdown", 561 | "metadata": {}, 562 | "source": [ 563 | "### a. Escolhido aleatoriamente uma observação do data set, qual a probabilidade de termos uma mulher fumante? ### \n", 564 | "\n", 565 | "1) Identificar a probabilidade de ser mulher + fumante" 566 | ] 567 | }, 568 | { 569 | "cell_type": "code", 570 | "execution_count": 59, 571 | "metadata": {}, 572 | "outputs": [ 573 | { 574 | "data": { 575 | "text/plain": [ 576 | "(244, 8)" 577 | ] 578 | }, 579 | "execution_count": 59, 580 | "metadata": {}, 581 | "output_type": "execute_result" 582 | } 583 | ], 584 | "source": [ 585 | "# identificando o tamanho do dataset(nosso campo amostral) (244 observações)\n", 586 | "tips.shape" 587 | ] 588 | }, 589 | { 590 | "cell_type": "code", 591 | "execution_count": 45, 592 | "metadata": {}, 593 | "outputs": [ 594 | { 595 | "data": { 596 | "text/plain": [ 597 | "genero fumante\n", 598 | "Feminino nao 54\n", 599 | " sim 33\n", 600 | "Masculino nao 97\n", 601 | " sim 60\n", 602 | "Name: fumante, dtype: int64" 603 | ] 604 | }, 605 | "execution_count": 45, 606 | "metadata": {}, 607 | "output_type": "execute_result" 608 | } 609 | ], 610 | "source": [ 611 | "# quantas mulheres são fumantes ? (número de )\n", 612 | "tips.groupby('genero')['fumante'].value_counts()" 613 | ] 614 | }, 615 | { 616 | "cell_type": "code", 617 | "execution_count": 58, 618 | "metadata": {}, 619 | "outputs": [ 620 | { 621 | "name": "stdout", 622 | "output_type": "stream", 623 | "text": [ 624 | "A probabilidade de ser mulher e fumante é de: 13.52\n" 625 | ] 626 | } 627 | ], 628 | "source": [ 629 | "#probabilidade mulher + fumante\n", 630 | "print(\"A probabilidade de ser mulher e fumante é de:\", \"%.2f\" %((33/244)*100))" 631 | ] 632 | }, 633 | { 634 | "cell_type": "markdown", 635 | "metadata": {}, 636 | "source": [ 637 | "### b. E quando é mais provável ter clientes fumantes (independente do sexo), durante a semana ou aos finais de semana?" 638 | ] 639 | }, 640 | { 641 | "cell_type": "code", 642 | "execution_count": 89, 643 | "metadata": {}, 644 | "outputs": [ 645 | { 646 | "data": { 647 | "text/plain": [ 648 | "dia fumante\n", 649 | "dom nao 57\n", 650 | " sim 19\n", 651 | "qui nao 45\n", 652 | " sim 17\n", 653 | "sab nao 45\n", 654 | " sim 42\n", 655 | "sex sim 15\n", 656 | " nao 4\n", 657 | "Name: fumante, dtype: int64" 658 | ] 659 | }, 660 | "execution_count": 89, 661 | "metadata": {}, 662 | "output_type": "execute_result" 663 | } 664 | ], 665 | "source": [ 666 | "#como está distribuído os clientes fumantes e não fumantes ao longo dos dias da semana ?\n", 667 | "tips.groupby('dia')['fumante'].value_counts()" 668 | ] 669 | }, 670 | { 671 | "cell_type": "code", 672 | "execution_count": 129, 673 | "metadata": {}, 674 | "outputs": [ 675 | { 676 | "data": { 677 | "text/html": [ 678 | "
\n", 679 | "\n", 692 | "\n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | "
total_contagorjetagenerofumantediahorariopessoas_mesatempo_permanenciafds
016.991.01Femininonaodomjantar241sim
110.341.66Masculinonaodomjantar340sim
221.013.50Masculinonaodomjantar349sim
323.683.31Masculinonaodomjantar243sim
424.593.61Femininonaodomjantar434sim
\n", 770 | "
" 771 | ], 772 | "text/plain": [ 773 | " total_conta gorjeta genero fumante dia horario pessoas_mesa \\\n", 774 | "0 16.99 1.01 Feminino nao dom jantar 2 \n", 775 | "1 10.34 1.66 Masculino nao dom jantar 3 \n", 776 | "2 21.01 3.50 Masculino nao dom jantar 3 \n", 777 | "3 23.68 3.31 Masculino nao dom jantar 2 \n", 778 | "4 24.59 3.61 Feminino nao dom jantar 4 \n", 779 | "\n", 780 | " tempo_permanencia fds \n", 781 | "0 41 sim \n", 782 | "1 40 sim \n", 783 | "2 49 sim \n", 784 | "3 43 sim \n", 785 | "4 34 sim " 786 | ] 787 | }, 788 | "execution_count": 129, 789 | "metadata": {}, 790 | "output_type": "execute_result" 791 | } 792 | ], 793 | "source": [ 794 | "#classificando em dia de semana e final de semana\n", 795 | "# criando uma nova variável com a classificação\n", 796 | "tips['fds'] = tips['dia'].map({'dom':'sim','sab':'sim', 'qui':'nao','sex':'nao'})\n", 797 | "tips.head()" 798 | ] 799 | }, 800 | { 801 | "cell_type": "code", 802 | "execution_count": 131, 803 | "metadata": {}, 804 | "outputs": [ 805 | { 806 | "data": { 807 | "text/plain": [ 808 | "fds fumante\n", 809 | "nao nao 49\n", 810 | " sim 32\n", 811 | "sim nao 102\n", 812 | " sim 61\n", 813 | "Name: fumante, dtype: int64" 814 | ] 815 | }, 816 | "execution_count": 131, 817 | "metadata": {}, 818 | "output_type": "execute_result" 819 | } 820 | ], 821 | "source": [ 822 | "#encontrando a distribuição de fumantes por final de semana\n", 823 | "tips.groupby('fds')['fumante'].value_counts()" 824 | ] 825 | }, 826 | { 827 | "cell_type": "code", 828 | "execution_count": 136, 829 | "metadata": {}, 830 | "outputs": [ 831 | { 832 | "data": { 833 | "text/plain": [ 834 | "sim 163\n", 835 | "nao 81\n", 836 | "Name: fds, dtype: int64" 837 | ] 838 | }, 839 | "execution_count": 136, 840 | "metadata": {}, 841 | "output_type": "execute_result" 842 | } 843 | ], 844 | "source": [ 845 | "#distribuição dos clientes no final de semana\n", 846 | "tips['fds'].value_counts()" 847 | ] 848 | }, 849 | { 850 | "cell_type": "markdown", 851 | "metadata": {}, 852 | "source": [ 853 | "Quando olhamos especifamente o final de semana nosso espaço amostral ($\\omega $) de clientes se altera para 163. Por isso para achar a probabilidade de ser cliente e fumante no final de semana é: $ \\frac{totalFumantesFDS}{clientesFDS}$" 854 | ] 855 | }, 856 | { 857 | "cell_type": "code", 858 | "execution_count": 139, 859 | "metadata": {}, 860 | "outputs": [ 861 | { 862 | "name": "stdout", 863 | "output_type": "stream", 864 | "text": [ 865 | "A probabilidade de se aleatoriamente escolhermos uma pessoal fumante no final de semana é de: 0.37423312883435583\n" 866 | ] 867 | } 868 | ], 869 | "source": [ 870 | "#probabilidade de fumantes\n", 871 | "print('A probabilidade de se aleatoriamente escolhermos uma pessoal fumante no final de semana é de:' ,61/163)" 872 | ] 873 | } 874 | ], 875 | "metadata": { 876 | "kernelspec": { 877 | "display_name": "Python 3", 878 | "language": "python", 879 | "name": "python3" 880 | }, 881 | "language_info": { 882 | "codemirror_mode": { 883 | "name": "ipython", 884 | "version": 3 885 | }, 886 | "file_extension": ".py", 887 | "mimetype": "text/x-python", 888 | "name": "python", 889 | "nbconvert_exporter": "python", 890 | "pygments_lexer": "ipython3", 891 | "version": "3.7.3" 892 | } 893 | }, 894 | "nbformat": 4, 895 | "nbformat_minor": 2 896 | } 897 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-minimal 2 | title: Ciência de Dados 3 | description: Materiais disponíveis sobre Ciência de Dados das Pyladies São Paulo 4 | logo: logo.jpg 5 | -------------------------------------------------------------------------------- /licenca.md: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /logo.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PyLadiesSP/data-science/6432209c4f730c6fcaafbccea5e2d87f83b76bc9/logo.jpg --------------------------------------------------------------------------------